Message from Directorcripac.ia.ac.cn/en/attached/file/20160824/20160824150429_752.pdfDenoising Autoencoder” by PhD student Wei Tang won the Best student Paper Award of CCCV2015,

CENTER FOR RESEARCH ON INTELLIGENT PERCEPTION AND COMPUTING

Message from DirectorThe Center for Research on Intelligent Perception and Computing (CRIPAC) is an independent department at the Institute of Automation, Chinese Academy of Sciences (CASIA). With focuses on three key research platforms, namely SIR (Smart Identity Recognition), DIG (Data Intelligence Gathering) and iSEE (Intelligent Scene Exploration and Evaluation), CRIPAC Biometric Acquisition and Recognition aims to establish intelligent information processing systems that integrate the power of humans, computers and things, and to provide scientific theories, key technologies, pioneering applications and innovative talents for the era of intelligence.The past year 2015 witnessed the flourishing developments of our center. Currently, CRIPAC has an energetic team including four full professors, six associate professors or senior engineers, seven assistant professors, two post-doctoral research fellows, twenty-six technicians and sixty-two postgraduate students. The research fellows in CRIPAC all strive to be the backbone of

the science and technology industry in China. Prof. Liang Wang was granted the National Science Fund for Distinguished Young Scholars and he was rated as excellent in the final assessment for CAS Hundred Talent Program, Prof. Kaiqi Huang was elected as Young top-notch talent for Ten Thousand Talent Program and excellent member of CAS Youth Innovation Promotion Association, Associate Prof. Ran He won the Second Prize of Wu Wenjun Artificial Intelligence Science and Technology Innovation Award, ACM Beijing Rising Star Award and member of CAS Youth Innovation Promotion Association, Associate Prof. Yongzhen Huang won the Tencent Rhino Bird Excellent Prize, Prof. Tieniu Tan, Prof. Liang Wang, Prof. Kaiqi Huang, Prof. Zhenan Sun, Associate Prof. Ran He and Associate Prof. Yongzhen Huang were elected as members of CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT). With regard to funding applications, CRIPAC has obtained two more grants, Vision Pattern Analysis and Understanding (National Science Fund for Distinguished Young Scholars) and Brain Functional Connection Mapping and Brain-Like Intelligence Research (CAS Advanced Research Project). CRIPAC members remained productive in 2015. Sixteen journal papers were published. The paper titled “An Adaptive Ensemble Classifier for Steganalysis Based on Dynamic Weighted Fusion” by PhD student Xikai Xu won the Best Paper Award of CSPS 2015. The paper titled “1000 Fps Highly Accurate Eye Detection with Stacked Denoising Autoencoder” by PhD student Wei Tang won the Best student Paper Award of CCCV2015, and the paper titled “Fusion of Face and Iris Biometrics on Mobile Devices Using Near-infrared Images” by PhD student Qi Zhang won the Best Paper Award of CCBR2015. In the realm of key techniques, we have made breakthroughs in face recognition based on deep learning, iris recognition, gait recognition, action recognition, image recognition and image retrieval. When it comes to applying the latest technology, the large-scale crowd surveillance system has been successfully deployed in the frontlines of anti-terrorist and safeguard stability, face recognition system has been used in internet finance and iris recognition system has been employed for numerous tourist sites. In terms of technology transfer and industrialization, Tianjin Academy for Intelligent Recognition Technologies (TAiFRT) was officially established in Tianjin Binhai New Area. This academy serves as the industrialization platform for research fellows in CRIPAC. TAiFRT aims to make substantial contributions to Beijing-Tianjin-Hebei integration. With respect to industry alliance, the China Biometrics Alliance (CBA) now has a total of 85 member companies. The Big Image and Video Data Alliance (BIDA) has attracted a total of 58 member companies. These alliances will build up a coordinative innovation platform for manufacturing, studying, researching and deploying, and therefore speed up the industrialization process of science and technology outputs in China. CRIPAC is always keen on establishing academic and industrial links, and is eager to collaborate with worldwide leading research institutes, companies, investigators and governments to promote the development of intelligence and its applications for the well-being of humanity. Director of Center for Research on Intelligent Perception and Computing


Table of Contents

Introduction 1

Organizational Structure 2

People 3

Faculty 3

Administrative Staff 15

Technical Staff 16

Postdoctors 17

Postgraduates 18

Research Directions 20

Biometrics 20

Image and Video Analysis 20

Big Data and Multi-modal Computing 20

Content Security and Authentication 21

Sensing and Information Acquisition 21

Research Facilities 22

Representative Research Progress 24

Overview of Current Research Projects 27

Publications 33

Awards and Recognitions 39

Professional Activities 40

Academic Exchanges and Cooperations 42

Conferences and Workshops 42

Visits 42

Research Talks by External Visitors 44

Memorabilia in 2015 45

1


Introduction

The Center for Research on Intelligent Perception and Computing (CRIPAC) is affiliated to the Institute of Automation,

Chinese Academy of Sciences (CAS). Focusing on the theory and technology of intelligent perception and computing, the

Center aims at conducting cutting-edge research and applications, and strives to build a world-class research organization

in terms of personnel training, technological innovation and technology transfer. Currently, the Center’s work mainly

concerns information sensing, intelligent recognition, content security, system integration and applications.

The Center currently has seventeen faculty members including one CAS academician, two CAS “Hundred Talents

Program” recipients, two NSFC Distinguished Youth Fund awardee and one NSFC Excellent Youth Fund winner. These

research faculty members are supported by a total of twenty eight administrative and technical staff members, and

around seventy over researchers including post-doctoral research fellows, PhD and MSc students, visiting scholars and

intern students.

The Center has a large number of quality publications including more than 600 journal and conference papers, and

more than 90 patents. CRIPAC also has won many prestigious national and international awards in recent years,

including the Second Prize of the National Award for Technology Invention, the Second Prize of the National Award

for Science & Technology Progress, the Second Prize of the National Award for Natural Science, the First Prize of the

Beijing Science and Technology and so on. Moreover, CRIPAC achieves the best performance on several world-leading

competitions, e.g., champions of object detection in PASCAL VOC 2010 and 2011, and the first place in a series of

NICE competitions.

2


Organizational Structure

Beijing Vistek TechnologyCo.,Ltd.

Beijing IrisKing Co., Ltd.

Tianjin Academy for Intelligent Recognition Technologies

Chinese Big Image and video Data Alliance (BIDA)

Chinese BiometricsAlliance (CBA)

Image and Video Analysis Group

Biometrics Group

Big Data and Multi-modal Computing Group

General Office

startup companies

Alliances

DirectorDeputy DirectorChief Engineer

CRIPAC

Administration Committee

3


People

FacultyXiaotang Chen

Dr. Xiaotang Chen received the BSc degree from Xidian University in 2008. She received the

PhD degree in Pattern Recognition and Intelligent Systems from the Institute of Automation,

Chinese Academy of Sciences (CASIA) in 2013. In July 2013, Dr. Chen joined the Center for

Research on Intelligent Perception and Computing (CRIPAC) as an Assistant Professor. Her

major research interests include computer vision and pattern recognition. Dr. Chen is currently a

member of IEEE and serves as the program committee member of several conferences.

Selected Publications

1. Xiaotang Chen, Kaiqi Huang and Tieniu Tan, “Object Tracking Across Non-Overlapping Views by Learning Inter-

Came ra Transfer Models,” Pattern Recognition, vol. 47, no. 3, pp. 1126-1137, 2014.

2. Xiaotang Chen, Kaiqi Huang and Tieniu Tan, “Learning the Three Factors of a Non-Overlapping Multi-Camera

Network Topology,” Proc. Chinese Conference on Pattern Recognition, pp. 104-112, September 2012, Beijing, China.

3. Xiaotang Chen, Kaiqi Huang and Tieniu Tan, “Object Tracking Across Non-Overlapping Cameras Using Adaptive

Models,” Proc. ACCV Workshop on Detection and Tracking in Challenging Environments, pp. 464-477, November

2012, Korea.

4. Xiaotang Chen, Kaiqi Huang and Tieniu Tan, “Direction-based Stochastic Matching for Pedestrian Recognition in

Non-Overlapping Cameras,” Proc. IEEE International Conference on Image Processing, pp. 2065-2068 September

2011, Belgium.

Jing Dong

PhD, Associate Professor. Dr. Jing Dong received her BSc in Electronic Information Science and

Technology from Central South University in 2005 and her PhD in Pattern Recognition from

the Graduate University of Chinese Academy of Sciences. Since July 2010, Dr. Dong has joined

the National Laboratory of Pattern Recognition (NLPR), where she is currently an Associate

Professor. Her research interests include pattern recognition, image processing and digital image

forensics. She has published over 30 academic papers and she is a member of CCF, CAAI, IEEE,

IEEE Computer Science Society, Signal processing Society and Communication Society. She also serves as the deputy

general of Chinese Association for Artificial Intelligence and an IEEE Volunteer leader in R10 and in Beijing Section

from many aspects of academic activities.


1. Zairan Wang, Jing Dong and Wei Wang, “Quantization Based Watermarking Methods Against Valumetric

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Distortions,” [J] International Journal of Automation and Computing, 2015.

2. Wei Wang, Jing Dong and Tienu Tan, “Exploring DCT Coefficient Quantization Effects for Local Tampering

Detection,” IEEE Trans. on Information Forensics and Security, vol. 9, no. 10, pp. 1653-1666, 2014.

3. Jing Dong, Wei Wang and Tieniu Tan, “CASIA Image Tampering Detection Evaluation Database,” Proc. IEEE

China Summit and International Conference on Signal and Information Processing, pp. 422-426, July 2013,

Beijing, China.

4. Jing Dong and Tieniu Tan, “Blind Image Steganalysis Based on Run-length Histogram Analysis,” Proc. IEEE

International Conference on Image Processing, pp. 2064- 2067, October 2008, USA.

5. Jing Dong and Tieniu Tan, “Security Enhancement of Biometrics, Cryptography and Data Hiding by Their

Combinations,” Proc. 5th International Conference on Visual Information Engineering, pp. 239-244, July, 2008.

Ran He

Dr. Ran He received the BSc degree in Computer Science from Dalian University of Technology,

the MSc degree in Computer Science from Dalian University of Technology, and PhD degree in

Pattern Recognition and Intelligent Systems from the Institute of Automation, Chinese Academy

of Sciences in 2001, 2004 and 2009, respectively. Since September 2010, Dr. He has joined

NLPR where he is currently an Associate Professor. He is currently a member of IEEE and serves

as an associate editor of Elsevier Neurocomputing (2011- ) and IET Image process (2012).

He has widely published at highly ranked international journals, such as IEEE Trans. on Pattern Analysis and Machine

Intelligence (TPAMI), IEEE Trans. on Image Processing (TIP), IEEE Trans. on Knowledge and Data Engineering

(TKDE), IEEE Trans. on Neural Network and Learning System (TNNLS), and Neural Computation (NECO), and

leading international conferences, such as Computer Vision and Pattern Recognition (CVPR) and the AAAI Conference

on Artificial Intelligence (AAAI).


1. Kaiye Wang, Ran He, Liang Wang, Wei Wang, Tieniu Tan, “Joint Feature Selection and Subspace Learning for

Cross-modal Retrieval,” IEEE Trans. on Pattern Analysis and Machine Intelligence, preprint, 2016.

2. Ran He, Tieniu Tan and Liang Wang, “Robust Recovery of Corrupted Low-Rank Matrix by Implicit Regularizers,”

IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 36, no. 4, pp. 770-783, 2014.

3. Ran He, Wei-Shi Zheng, Tieniu Tan and Zhenan Sun, “Half-Quadratic Based Iterative Minimization for Robust

Sparse Representation,” IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 36, no. 2, pp. 261-

275, 2014.

4. Ran He, Wei-Shi Zheng, Bao-Gang Hu and Xiang-Wei Kong, “Two-stage Nonnegative Sparse Representation for

Large-scale Face Recognition,” IEEE Trans. on Neural Network and Learning System (TNNLS), , vol. 24, no. 1,

pp. 35-46, 2013.

5. Ran He, Wei-Shi Zheng and Bao-Gang Hu, “Maximum Correntropy Criterion for Robust Face Recognition,” IEEE

Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 33, no. 8, pp.1561-1576, 2011.

5


Guangqi Hou

Dr. Guangqi Hou received his BSc, MSc and PhD degree from Beijing Institute of Technology,

China in 2001, 2004 and 2011, respectively. He was a research fellow at the Institute of

Automation, Chinese Academy of Sciences from 2011 to 2013. In July 2013, Dr. Hou joined the

National Laboratory of Pattern Recognition (NLPR) as an Assistant Professor, and became an

Associate Professor since Nov. 2014.

His current research focuses on Optical Imaging, Computational Photography, Light Field

Photography, Pattern Recognition and Computer Vision. He is a member of IEEE/SPIE, and a reviewer of TIFS/

Neuocomputing/Sensors/IJAC.


1. Chi Zhang, Guangqi Hou, Zhenan Sun and Tieniu Tan, “Efficient Auto-Refocusing of Iris Images for Light-Filed

Cameras,” Proc. International Joint Conference on Biometrics, Sep 29-Oct 2, 2014, Florida USA.

2. Jie Gui, Zhenan Sun, Guangqi Hou and Tieniu Tan, “An Optimal Set of Code Words and Correntropy for Rotated

Least Squares Regression,” Proc. International Joint Conference on Biometrics, Sep 29-Oct 2, 2014, Florida USA.

3. Shu Zhang, Guangqi Hou and Zhenan Sun, “Eyelash Removal Using Light Field Camera for Iris Recognition,”

Proc. Chinese Conference on Biometric Recognition, vol.8833, pp. 319-327, November 2014, Shengyang, China.

4. Chi Zhang, Guangqi Hou, Zhenan Sun, Tieniu Tan and Zhiliang Zhou, “ Light Field Photography for Iris Image

Acquisition,” Proc. Chinese Conference on Biometric Recognition, pp. 345-352, November 2013, Jinan, China

5. Guangqi Hou and Ping Wei, “Efficient Stereo Matching Scheme Based on Graph Cuts,” Journal of Beijing Institute

of Technology, Vol.4 2010.

Kaiqi Huang

Dr. Kaiqi Huang received his BSc and MSc from Nanjing University of Science and Technology,

China and obtained his PhD degree from Southeast University. He is a professor in National

Laboratory of Pattern Recognition (NLPR). He is an IEEE Senior member and deputy general

secretary of IEEE Beijing Section (2006-2008).

Dr. Huang has managed successfully many leading-edge research projects, including 4 National

Natural Science Foundation of China, 1 National High Technology Research and Development

Program ("863"Program) of China (No. 2009AA01Z318), and several international collaborative projects. He has served

as a member of the Program Committee for over 40 international conferences; he is an executive team member of IEEE

SMC Cognitive Computing Committee as well as Associate Editor of IEEE Trans. on Systems, Man, and Cybernetics:

Systems (TSMC), International Journal of Image and Graphics (IJIG), Electronic Letters on Computer Vision and Image

Analysis (ELCVIA) and a Guest Editor of Signal Processing special issue on Security. He has obtained some awards

including The National Science and Technology Progress Award in 2011, Beijing Science & Technology Star Award and

Beijing Science and Technology Progress Award in 2008.

His current research interests include visual surveillance, digital image processing, pattern recognition and biological

6


based vision and so on. He has published over 100 papers in important international journals and conferences such as

IEEE TIPAMI, T-IP, T-SMC-B, TCSVT, Pattern Recognition (PR), Computer Vision and Image Understanding (CVIU),

ECCV, CVPR, ICIP and ICPR. He received the Best Student Paper Awards from ACPR10, the winner prizes of the

object detection task in both PASCAL VOC'10 and PASCAL VOC'11, the honorable mention prize of the classification

task in PASCAL VOC'11, the winner of the classification task (additional data) in ImageNet-ILSVRC 2014.


1. Kaiqi Huang, Chong Wang and Dacheng Tao, “High-OrderTopology Modeling of Visual Words for Image

Classification,” IEEE Trans. on ImageProcessing, vol. 24, no.11, pp. 3598-3608, 2015.

2. Kaiqi Huang, Yeying Zhang and Tieniu Tan, “A Discriminative Model of Motion and Cross Ratio for View-

Invariant Action Recognition,” IEEE Trans. on Image Processing, vol. 21, no. 4, pp. 2187-2197, 2012.

3. Kaiqi Huang, Dacheng Tao, Yuanyan Tang, Xuelong Li and Tieniu Tan, “Biologically Inspired Features for Scene

Classification in Video Surveillance,” IEEE Trans. on Systems, Man and Cybernetics, Part B, vol. 41,no. 1, pp.

307-313, 2011.

4. Kaiqi Huang and Tieniu Tan, “Vs-star: A Visual Interpretation System for Visual Surveillance,” Pattern Recognition

Letters, vol. 31, no. 14, pp. 2265-2285, 2010.

5. Kaiqi Huang, Shiquan Wang, Tieniu Tan and Stephen J. Maybank, “Human Behavior Analysis Based on a New

Motion Descriptor,” IEEE Trans. on Circuits and Systems for Video Technology, vol.19, no.12, pp. 1830-1840, 2009.

Yongzhen Huang

Dr. Yongzhen Huang received his B.E. degree from Huazhong University of Science and

Technology (HUST) in 2006, and his PhD. degree from Institute of Automation, Chinese

Academy of Sciences (CASIA) in 2011. Then he joined National Laboratory of Pattern

Recognition (NLPR) as an Assistant Professor in July 2011, and became an Associated

Professor since Nov. 2013. His research interests include computer vision, pattern recognition,

machine learning, and computational visual cognition. He has published one book and more

than 50 papers in international journals and conferences such as IEEE TPAMI, IEEE TMSC-B, IEEE TCSVT, TMM,

Neurocomputing, CVPR, ICCV, NIPS, ICPR, ICIP. He has obtained several honors and awards, including the Excellent

Doctoral Thesis of Chinese Association for Artificial Intelligence (2012), the Best Student Paper of Chinese Conference

on Computer Vision (2015), the Champion of PASCAL VOC Challenges on object detection (2010 and 2011), the

Runner-up of PASCAL VOC Challenges on object classification (2011), and the Champion of Internet Contest for Cloud

& Mobile Computing on Human Segmentation with 230,000RMB (2013), the Second Prize and the Prize of Highest

Accuracy with Low Energy in LPIRC (Low-Power Image Recognition Challenge) (2015). Dr. Huang is currently a

member of IEEE. He has served as Associate Editor of Neurocomputing, the web chair of AVSS2012, the publicity

chair of CCPR2012, the program committee member of 6 conferences, and the peer reviewer of over 20 journals and

conferences.

7



1. Yongzhen Huang and Tieniu Tan, Feature coding for Image Representation and Recognition, Springer, invited

book, 2015.

2. Yongzhen Huang, Zifeng Wu, Liang Wang and Tieniu Tan, “Feature Coding in Image Classification: A

Comprehensive Study,” IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 36, no. 3, pp. 493-

506, 2014.

3. Yongzhen Huang, Kaiqi Huang, Dacheng Tao, Tieniu Tan and Xuelong Li, “Enhanced Biologically Inspired Model

for Object Recognition,” IEEE Trans. on Systems, Man and Cybernetics, Part B, vol. 41, no. 6, pp. 1668-1680, 2011

4. Yongzhen Huang, Kaiqi Huang, Yinan Yu and Tieniu Tan, “Salient Coding for Image Classification,” Proc.

International Conference on Computer Vision and Pattern Recognition, pp. 1753-1760, June 2011, USA.

5. Yongzhen Huang, Kaiqi Huang, Chong Wang and Tieniu Tan, “Exploring Relations of Visual Codes for Image

Classification,” Proc. International Conference on Computer Vision and Pattern Recognition, pp. 1649-1656, June

2011, USA.

Haiqing Li

Dr. Haiqing Li received the B.E. degree in automation from the Sichuan University. He received

the PhD degree in Pattern Recognition and Intelligent Systems from the Institute of Automation,

Chinese Academy of Sciences (CASIA) in 2009, and 2015, respectively. He is currently an

Assistant Professor with the Center for Research on Intelligent Perception and Computing

(CRIPAC), National Laboratory of Pattern Recognition (NLPR), CASIA. His major research

interests include biometrics, computer vision, pattern recognition and machine learning.


1. Haiqing Li, Zhenan Sun, Man Zhang, Libin Wang, Lihu Xiao and Tieniu Tan, “A brief survey on recent progress

in iris recognition,” Proc. Chinese Conference on Biometric Recognition, vol.8833, pp. 288-300, November 2014,

Shenyang, China.

2. Haiqing Li, Zhenan Sun and Tieniu Tan, “Robust Iris Segmentation Based on Learned Boundary Detectors,” Proc.

International Conference on Biometrics, pp. 317-322, March 2012, India.

3. Haiqing Li, Zhenan Sun and Tieniu Tan, “Accurate Iris Localization Using Contour Segments,” Proc. International

Conference on Pattern Recognition, pp. 3398-3401, November 2012, Japan.

Zhenan Sun

Dr. Zhenan Sun received the BSc degree in Industrial Automation from Dalian University of

Technology, the MSc degree in Systems Engineering from Huazhong University of Science and

Technology, and PhD degree in Pattern Recognition and Intelligent Systems from the Institute of

Automation, Chinese Academy of Sciences in 1999, 2002 and 2006, respectively. Since March

2006, Dr. Sun has joined the National Laboratory of Pattern Recognition (NLPR) where he is

8


currently a Professor. His current research focuses on biometric recognition for personal identification, image processing

and computer vision.


1. Zhenan Sun, Hui Zhang, Tieniu Tan and Jianyu Wang, “Iris Image Classification Based on Hierarchical Visual

Codebook,” IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI) , vol. 36, no. 6, pp. 1120-1133,

2014.

2. Zhenan Sun, Libin Wang and Tieniu Tan, “Ordinal Feature Selection for Iris and Palmprint Recognition,” IEEE

Trans. on Image Processing, vol. 23, no. 9, pp. 3922-3934, 2014.

3. Zhenan Sun and Tieniu Tan, “Ordinal Measures for Iris Recognition,” IEEE Trans. on Pattern Analysis and

Machine Intelligence (PAMI), vol. 31, no. 12, pp. 2211-2226, 2009.

4. Zhenan Sun, Tieniu Tan, Yunhong Wang and Stan Z. Li, “Ordinal Palmprint Representation for Personal

Identification,” Proc. IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 279-284, June

2005, USA.

Tieniu Tan

Dr. Tieniu Tan received his BSc degree in electronic engineering from Xi'an Jiaotong University,

China, in 1984, and his MSc and PhD degrees in electronic engineering from Imperial College

London, U.K., in 1986 and 1989, respectively. In October 1989, he joined the Computational

Vision Group at the Department of Computer Science, The University of Reading, Reading, U.K.,

where he worked as a Research Fellow, Senior Research Fellow and Lecturer. In January 1998,

he returned to China to join the National Laboratory of Pattern Recognition (NLPR), Institute

of Automation of the Chinese Academy of Sciences (CAS), Beijing, China, where he is currently a Professor and the

director of Center for Research on Intelligent Perception and Computing (CRIPAC), and was former director (1998-

2013) of the NLPR and Director General of the Institute (2000-2007). He is currently also Vice President of the Chinese

Academy of Sciences. He has published 13 edited books or monographs and more than 500 research papers in refereed

international journals and conferences in the areas of image processing, computer vision and pattern recognition. His

H-index is 66 (as of March 2015). His current research interests include biometrics, image and video understanding, and

information content security.

Dr. Tan is a Fellow of CAS, TWAS (The World Academy of Sciences for the advancement of science in developing

countries), IEEE and IAPR (the International Association of Pattern Recognition), and an International Fellow of the

UK Royal Academy of Engineering. He has served as chair or program committee member for many major national

and international conferences. He is or has served as Associate Editor or member of editorial boards of many leading

international journals including IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), IEEE

Transactions on Automation Science and Engineering, IEEE Transactions on Information Forensics and Security,

IEEE Transactions on Circuits and Systems for Video Technology, Pattern Recognition, Pattern Recognition Letters,

Image and Vision Computing, etc. He is Editor-in-Chief of the International Journal of Automation and Computing.

9


He was founding chair of the IAPR Technical Committee on Biometrics, the IAPR-IEEE International Conference on

Biometrics, the IEEE International Workshop on Visual Surveillance and Asian Conference on Pattern Recognition

(ACPR). He is currently the Deputy President of Chinese Artificial Intelligence Association. He has served as

the President of the IEEE Biometrics Council. He has given invited talks and keynotes at many universities and

international conferences, and has received many national and international awards and recognitions.




2. Zhenan Sun, Hui Zhang, Tieniu Tan and Jianyu Wang, “Iris Image Classification Based on Hierarchical Visual

Codebook,” IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI) , vol. 36, no. 6, pp. 1120-1133,

2014.



506, 2014.

4. Tieniu Tan, Xiaobo Zhang, Zhenan Sun and Hui Zhang, “Noisy Iris Image Matching by Using Multiple Cues,”

Pattern Recognition Letters,vol. 33, no. 8, pp. 970-977, 2012.

5. Tieniu Tan, Zhaofeng He and Zhenan Sun, “Efficient and Robust Segmentation of Noisy Iris Images for Non-

cooperative Iris Recognition,” Image and Vision Computing, vol. 28, no. 2, pp. 223-230, 2010.

Liang Wang

Dr. Liang Wang received both the B. Eng. and M. Eng. degrees from Anhui University in 1997

and 2000 respectively, and the PhD degree from the Institute of Automation, Chinese Academy

of Sciences (CAS) in 2004. From 2004 to 2010, he worked as a Research Assistant at Imperial

College London, United Kingdom and Monash University, Australia, a Research Fellow at the

University of Melbourne, Australia, and a lecturer at the University of Bath, United Kingdom,

respectively. Currently, he is a full Professor of Hundred Talents Program at the National Lab of

Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, P. R. China.

His major research interests include machine learning, pattern recognition and computer vision. He has widely published

at highly-ranked international journals such as IEEE TPAMI and IEEE TIP, and leading international conferences such

as CVPR, ICCV and ICDM. He has obtained several honors and awards such as the Special Prize of the Presidential

Scholarship of Chinese Academy of Sciences. He is currently a Senior Member of IEEE and a Fellow of IAPR, as well

as a member of BMVA. He is an associate editor of IEEE Transactions on Cybernetics, International Journal of Image

and Graphics, Neurocomputing and so on. He is a guest editor of 9 special issues, a co-editor of 6 edited books, a co-

program chair of 3 conferences, and a co-chair of 12 international workshops.


1. Liang Wang and Chuan Li, “Spectrum-Based Kernel Length Estimation for Gaussian Process Classification,” IEEE

10


Trans. on Cybernetics, vol. 44, no.6, pp. 805-816, 2014.



3. Worapan Kusakunniran, Qiang Wu, Hongdong Li and Liang Wang, “Recognizing Gaits Across Views Through

Correlated Motion Co-Clustering,” IEEE Trans. on Image Processing, vol. 23, no. 2, pp. 696-709, 2014.



506, 2014.

5. Chen Wang, Junping Zhang, Liang Wang, Jian Pu, and Xiaoru Yuan, “Human Identification Using Temporal

Information Preserving Gait Templates,” IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol.

34, no. 11,pp. 2164-2176, 2012.

Wei Wang

Dr. Wei Wang received the BSc degree in the Department of Automation from Wuhan University

in 2005, and the PhD degree in the School of Information Science and Engineering at the

Graduate University of Chinese Academy of Sciences (GUCAS) in 2011. Since July 2011, Dr.

Wang has joined the National Laboratory of Pattern Recognition (NLPR) where he is currently an

Assistant Professor. His research interests focus on computer vision, pattern recognition, visual

attention and deep learning.


1. Yan Huang, Wei Wang, Liang Wang, “Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-

Resolution,” Proc. Advances in Neural Information Processing Systems, pp. 235-243, December 2015, Quebec,

Canada.

2. Wei Wang, Yan Huang, Yizhou Wang and Liang Wang, “Generalized Autoencoder: A Neural Network Framework

for Dimensionality Reduction,” Proc. IEEE International Conference on Computer Vision and Pattern Recognition

Workshops, pp. 496-503, June 2014, Columbus, USA. (Best Paper)

3. Wei Wang, Cheng Chen, Yizhou Wang, Tingting Jiang, Fang Fang and Yuan Yao, “Simulating Human Saccadic

Scanpath on Natural Images,” Proc. IEEE International Conference on Computer Vision and Pattern Recognition,

pp. 441-448, June 2011, Colorado Springs, USA.

4. Wei Wang, Yizhou Wang, Qingming Huang and Wen Gao, “Measuring Visual Saliency by Site Entropy Rate,”

Proc. IEEE International Conference on Computer Vision and Pattern Recognition, pp. 2368-2375, June 2010, San

Francisco, USA.

5. Wei Wang, Yizhou Wang, Luoshe Huo, Qingming Huang, and Wen Gao, “Symmetric Segment-Based Stereo

Matching for Motion Blurred Images with Illumination Variations,” Proc. International Conference on Pattern

Recognition, pp.1-4, December 2008.

11


Wei Wang

Dr. Wei Wang received his B.E. in Computer Science and Technology from North China Electric

Power University in 2007 and his PhD in Pattern Recognition from the Graduate University of

Chinese Academy of Sciences. Since July 2012, Dr. Wang has joined the National Laboratory of

Pattern Recognition (NLPR) where he is currently an Assistant Professor. His research interests

include pattern recognition, image analysis and processing, and digital image forensics. He is a

member of CCF, IEEE, IEEE Computer Society, and Signal Processing Society.


1. Wei Wang, Jing Dong and Tienu Tan, “ Exploring DCT Coefficient Quantization Effects for Local Tampering

Detection,” IEEE Trans. on Information Forensics and Security, vol. 9, no. 10, pp. 1653-1666, 2014.

2. Wei Wang, Jing Dong and Tieniu Tan, “Exploring DCT Coefficient Quantization Effect for Image Tampering

Localization,” Proc. IEEE International Workshop on Information Forensics and Security, pp. 1-6, December 2011,

Brazil.

3. Wei Wang, Jing Dong and Tieniu Tan, “Tampered Region Localization of Digital Color Images Based on JPEG

Compression Noise,” Proc. International Workshop on Digital Watermarking, pp. 120-133, October 2010, Korea.

4. Wei Wang, Jing Dong and Tieniu Tan, “Image Tampering Detection Based on Stationary Distribution of Markov

Chain,” Proc. IEEE International Conference on Image Processing, pp. 2101-2104, September 2010, China.

5. Wei Wang, Jing Dong and Tieniu Tan, “Effective Image Splicing Detection Based On Image Chroma,” Proc. IEEE

International Conference on Image Processing, pp. 1257-1269, November 2009, Egypt.

Shu Wu

Dr. Shu Wu received the BSc degree from Hunan University, China, in 2004, the MSc degree

from Xiamen University, China, in 2007, and the PhD degree from the University of Sherbrooke,

Canada, in 2012, all in computer science. He is an Assistant Professor in the Center for Research

on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition

(NLPR), Institute of Automation, Chinese Academy of Sciences. His research interests include

data mining, recommendation systems, pervasive computing, and network data analytics. He is a

member of the IEEE, ACM and ACM SIGIR.


1. Qiang Liu, Shu Wu and Liang Wang, “COT: Contextual Operating Tensor for Context-aware Recommender

Systems,” Proc. AAAI Conference on Artificial Intelligence, pp. 203-209, January 2015, Austin, Texas, USA.

2. Shu Wu and Shengrui Wang, “Information-theoretic Outlier Detection for Large-scale Categorical Data,” IEEE

Trans. on Knowledge and Data Engineering (TKDE), VOL. 25, 2013.

3. Shu Wu and Shengrui Wang, “Parameter-free Outlier Detection for Large-scale Categorical Data,” Proc.

International Conference on Machine Learning and Data Mining (MLDM 2011), 2011.

4. Shu Wu and Shengrui Wang, “Rating-based Collaborative Filtering Combined with Additional Regularization,” Proc.

12


International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2011), 2011.

5. Shu Wu, Qingshan Jiang and Joshua Zhexue Huang, “A New Initialization Method for Clustering Categorical

Data,” Proc. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2007), 2007.

Junge Zhang

Dr. Junge Zhang received the BSc degree from China University of Geosciences (CUG) in 2008.

He received his PhD degree in Pattern Recognition and Intelligent Systems from the Institute of

Automation, Chinese Academy of Sciences (CASIA) in 2013. In July 2013, Dr. Zhang joined

the Center for Research on Intelligent Perception and Computing (CRIPAC) as an Assistant

Professor. His major research interests include computer vision, pattern recognition. Dr. Zhang

is currently a member of IEEE and a committee member of CCF YOCSEF. He served as the

publicity chair and the technical program committee member of several conferences, and the peer reviewer of over 10

international journals and conferences. In 2010 and 2011, he and his group members won the champion of PASCAL

VOC challenge on object detection and the second place on object classification.


1. Junge Zhang, Yongzhen Huang, Kaiqi Huang, Zifeng Wu and Tieniu Tan, “Data Decomposition and Spatial

Mixture Modeling for Part based Model,” Proc. Asian Conference on Computer Vision, vol. 7724, pp. 123-137,

November 2012, Korea.

2. Junge Zhang, Xin Zhao, Yongzhen Huang, Kaiqi Huang and Tieniu Tan, “Semantic Windows Mining in Sliding

Window Based Object Detection,” Proc. International Conference on Pattern Recognition, pp. 3264-3267,

November 2012, Japan.

3. Junge Zhang, Kaiqi Huang, Yinan Yu and Tieniu Tan, “Boosted Local Structured HOG-LBP for Object Localization,”

Proc. International Conference on Computer Vision and Pattern Recognition, pp. 1394-1400, June 2011, USA.

4. Junge Zhang, Yinan Yu, Shuai Zheng and Kaiqi Huang, “An Empirical Study of Visual Features for Part Based

Model,” Proc. Asian Conference on Pattern Recognition, 2011.

5. Junge Zhang, Yinan Yu, Yongzhen Huang, Chong Wang, Weiqiang Ren, Jinchen Wu, Kaiqi Huang and Tieniu

Tan, “Object Detection based on Data Decomposition, Spatial Mixture Modeling and Context,” International

Conference on Computer Vision Workshop on Visual Object Classes Challenge, 2011.

Man Zhang

Dr. Man Zhang received her BSc degree in Information Engineering from Beijing University of

Posts and Telecommunications (BUPT) and PhD degree in Computer Application Technology

from the Institute of Automation, Chinese Academy of Sciences (CASIA) in 2008 and 2013,

respectively. Since July 2013, Dr. Zhang has joined the Center for Research on Intelligent

Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition (NLPR)

where she is currently an Assistant Professor. Her research interests are image processing, pattern

13


recognition and biometrics. She is a member of IEEE. She also has served as an IEEE Volunteer and student delegate in

R10.


1. Man Zhang, Jing Liu, Zhenan Sun, et al, “The First ICB Competition on Iris Recognition,” Proc. International Joint

Conference on Biometrics, Sep 29- Oct 2, 2014, Florida USA.

2. Man Zhang, Zhenan Sun and Tieniu Tan, “Deformed Iris Recognition Using Bandpass Geometric Features and

Lowpass Ordinal Features,” Proc. International Conference on Biometrics, June 2013, Spain.

3. Man Zhang, Zhenan Sun and Tieniu Tan, “Perturbation-enhanced Feature Correlation Filter for Robust Iris

Recognition,” IET Biometrics, vol. 1, no. 1, pp. 37-45, 2012.

4. Man Zhang, Zhenan Sun and Tieniu Tan, “Deformable DAISY Matcher for Robust Iris Recognition,” Proc. IEEE

International Conference on Image Processing, pp. 3250-3253, September 2011, Belgium.

Zhang Zhang

Dr. Zhang Zhang received the BSc degree in Computer Science and Engineering from Hebei

University of Technology and the PhD degree in Pattern Recognition and Intelligent Systems

from the Institute of Automation, Chinese Academy of Sciences in 2002 and 2009, respectively.

From 2009 to 2010, he was a research fellow at Nanyang Technological University, Singapore.

In September 2010, Dr. Zhang joined the National Laboratory of Pattern Recognition (NLPR)

where he is currently an Associate Professor. His current research focuses on pattern recognition,

intelligent visual surveillance, human action and activity recognition. He is a member of the IEEE.


1. Zhang Zhang and Dacheng Tao, “Slow Feature Analysis for Action Recognition,” IEEE Trans. on Pattern Analysis

and Machine Intelligence (PAMI), vol.34, no. 3, pp. 436-450, 2012.

2. Zhang Zhang, Tieniu Tan and Kaiqi Huang, “An Extended Grammar System for Learning and Recognizing

Complex Visual Events,” IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), vol. 33, no. 2, pp.

240-255, 2011.

3. Zhang Zhang, Kaiqi Huang and Tieniu Tan, “Multi-Thread Parsing for Recognizing Complex Events in Videos,”

Proc. European Conference on Computer Vision, pp. 738-751, October 2008, France.

4. Zhang Zhang, Kaiqi Huang, Tieniu Tan and Liangsheng Wang, “Trajectory Series Analysis based Event Rule

Induction for Visual Surveillance,” Proc. IEEE International Conference of Computer Vision and Pattern

Recognition, pp.1-8, June 2007, USA.

14


Xin Zhao

Dr. Xin Zhao received his BSc degree from Anhui University of Technology in 2006 and his

PhD degree in Pattern Recognition and Intelligent System from the University of Science and

Technology of China in 2013. Since July 2013, Dr. Zhao has joined the National Laboratory

of Pattern Recognition (NLPR) as an Assistant Professor. His research interests are computer

vision, pattern recognition and data mining. He has published several academic papers in the

international conferences. He is a member of CCF, IEEE, and IEEE Signal Processing Society.

He won the Best Student Paper Award in the First Asian Conference on Pattern Recognition in 2011.


1. Xin Zhao, Yinan Yu, Yongzhen Huang, Kaiqi Huang and Tieniu Tan, “Feature Coding via Vector Difference for

Image Classification,” Proc. International Conference on Image Processing, October 2012, USA.

2. Xin Zhao, Kaiqi Huang and Tieniu Tan, “A Comparison Study on Kernel Based Online Learning for Moving

Object Classification,” Proc. Chinese Conference on Intelligent Visual Surveillance, pp. 17-20, December 2011,

Beijing, China.

3. Xin Zhao, Jianwei Ding, Kaiqi Huang and Tieniu Tan, “Global and Local Training for Moving Object Classification

in Surveillance-Oriented Scene,” Proc. Asian Conference on Pattern Recognition, pp. 681-685, November 2011,

China.

4. Xin Zhao, Weiqiang Ren, Kaiqi Huang and Tieniu Tan, “Online Codebook Reweighting Using Pairwise Constraints

for Image Classification,” Proc. Asian Conference on Pattern Recognition, pp. 662-666, November 2011, China.

15


Lixia Ma is currently the Director

of the General Office of CRIPAC.

She received her BSc degree

from Renmin University of China

(RUC) in 2005. She has joined

the National Laboratory of Pattern

Recognition (NLPR), Institute

of Automation of the Chinese

Academy of Sciences, where she is currently a senior

engineer. She is in charge of document management,

human resources, publici ty, academic activity

arrangement, regulations, editing, safety and so on.

Liping Yin i s cur rent ly an

administrative staff member of

CRIPAC. She received her BSc

degree from Beijing University

of Technology in 2008. She is in

charge of research projects and

fund management, document

management, conference organization, salary, expenditure

and reimbursement, and other general affairs.

Administrative StaffGuohua Chen is currently an


CRIPAC. He is in charge of the

general affairs of the Chinese

Biometr ics Al l iance(CBA),

including publicity, document

management of alliance members,

activity organization, etc.

R u i e G u o i s c u r r e n t l y a n


CRIPAC. She received her BSc

degree from Beijing University

of Posts and Telecommunications

(BUPT) in 2009. She is in charge

of the center achievements

(awards/ patents/ publications) management, annual

and monthly reports, device purchase and management,

network maintenance and technical support, conference

organization, reimbursement and other general affairs.

Chen Huang is currently an


CRIPAC. He is in charge of the

general affairs of the Chinese Big

Image and Video Data Alliance

(BIDA), including publicity,

document management of alliance

members, and activity organization.

16


Technical Staff

Ping Bai Di Cao Honglei Gao Zhaofeng He Zhichao JiangAssistant Engineer Assistant Engineer Assistant Engineer Algorithm Engineer Algorithm Engineer

Yunfeng Kang Xingguang Li Yaobo Li Jing Liu Yong Liu Engineer Algorithm Engineer Software Engineer Algorithm Engineer Assistant Engineer

Pengcheng Ma Yanan Qin Jianwen Wang Jingqiu Wang Lei WangAlgorithm Engineer Engineer Software Engineer Assistant Engineer Assistant Engineer

17


Xinhao Wang Zhen Wang Yue Wu Yuantao Xie Ping YangSoftware Engineer Software Engineer Hardware Engineer Software Engineer Assistant Engineer

Wei Yin Xu ZhangHardware Engineer Software Engineer

Postdoctors

Jie Gui Yunlian Sun

18


Postgraduates

PhD Candidates

Name Y e a r o f Enrollment Advisor Research Area

Di Miao 2010 Tieniu Tan Score level fusion of multibiometricsDong Wang 2010 Tieniu Tan Link prediction and analysis for social networksQi Li 2011 Tieniu Tan A study on facial landmark detection and face clusteringKangwei Liu 2011 Kaiqi Huang Visual structure models for object detection and recognizationYinlong Qian 2011 Tieniu Tan Feature learning for setganalysis via deep modelsLijun Cao 2012 Kaiqi Huang Wide area visual group analysis for real scenesYanhua Cheng 2012 Kaiqi Huang Large scale visual recognitionWeiYu Guo 2012 Tieniu Tan Modeling interests and behaviors of users in social mediaYan Huang 2012 Liang Wang Deep learning and cyber data understandingYueying Kao 2012 Kaiqi Huang Aesthetics in computer visionJinde Liu 2012 Tieniu Tan Behavior analysis in videoNianfeng Liu 2012 Tieniu Tan Biometric recognition based on computational imagingQiyue Yin 2012 Liang Wang Research on multi-view data analysis and applicationsShu Zhang 2012 Tieniu Tan Representation Learning for Video based Face RecognitionZhen Zhou 2012 Tieniu Tan Context-constrained Visual Data Analysis and ApplicationsMuhammad Rauf 2013 Liang Wang Deep LearningDong Cao 2013 Tieniu Tan Face recognition and attribute analysis in dynamic scenesYong Du 2013 Liang Wang Deep representation learning for action recognition

Lianrui Fu 2013 Kaiqi HuangHuman pose estimation based on structured representation and

learningZhen Jia 2013 Tieniu Tan Research on visual semantic understandingFei Liu 2013 Tieniu Tan Research on depth map enhancement based on light field cameraHongsong Wang 2013 Liang Wang Deep learning in visual computingWei Tang 2013 Liang Wang Large-scale visual computing and its applicationsDangwei Li 2013 Kaiqi Huang Multi-camera trackingPeng Bo 2013 Tieniu Tan Research on passive multimedia forensicsQiang Liu 2013 Liang Wang Multimedia data mining of the websJian Liang 2013 Tieniu Tan Localization on visual targetsJun Tian 2013 Tieniu Tan Short text classification research based on deep neural networksQiang Cui 2013 Liang Wang Mining from multi-Model and multi-view dataDeepak kumar Jain 2014 Kaiqi Huang Human behavior analysisYong Gwon Ri 2014 Tieniu Tan Digital image forensicsWeihua Chen 2014 Kaiqi Huang Pedestrian identification in multi-camera scenesJingyu Liu 2014 Liang Wang Theory and method of mobile vision

19


Qi Zhang 2014 Tieniu TanFusion of face and iris biometrics on mobile devices using near-

infrared imagesJunbo Wang 2014 Tieniu Tan Attention-driven memory networks for computer visionChunshui Cao 2014 Tieniu Tan Research of feedback neuro networkYunbo Wang 2015 Zhenan Sun HCI based on light field imagingQiaozhe Li 2015 Kaiqi Huang Key techniques of visual analysisDa Li 2015 Tieniu Tan Large scale visual feature learning based on slow feature analysis

MSc Candidates

NameYear of Enrollment

Advisor Tentative Dissertation Title

Lingxiao Song 2013 Zhenan Sun Human-computer interaction based on light-field imagesRan Xu 2013 Kaiqi Huang Research on image super resolutionWeiqi Zhao 2013 Kaiqi Huang Action recognition in videoWenlong Cheng 2014 Liang Wang Data mining for multimedia content securityJiedong Hao 2014 Tieniu Tan Network image forensicsYabei Li 2014 Tieniu Tan Learning object detector from videosYan Li 2014 Kaiqi Huang large scale object recognitionYuqi Zhang 2014 Liang Wang Theory and application of deep learningLingxiao He 2014 Zhenan Sun Research on deep learning based iris recognitionWenzhen Huang 2014 Kaiqi Huang Machine learning in computer visionXiaoxiang Liu 2014 Tieniu Tan Heterogeneous biometric recognitionYunlong Wang 2015 Tieniu Tan Intelligent interaction and perception based on light-field image

analysisWeining Wang 2015 Liang Wang Cross-scene and cross-view gait recognitionJiabin Ma 2015 Liang Wang Brain-like visual computing and applicationsHongwen Zhang 2015 Zhenan Sun Research and applications of face image preprocessingHoujing Huang 2015 Kaiqi Huang Object detection and tracking in visual surveillanceHuikai Wu 2015 Kaiqi Huang Visual cognition and object recognitionYibo Hu 2015 Ran He Image segmentationYali Wang 2015 Zhenan Sun Personal identification based on behavioral features on mobile

devices

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Research Directions

BiometricsBiometrics group consists of 2 professors, 1 associate professor, 2 assistant professors, 2 post-doctors, 5 hired

engineering and project management staff members, 11 PhD and MSc students. The group undertakes projects from

to National Basic Research Program of China, National Sci. & Tech. Support Program, Instrument Developing Project

of the Chinese Academy of Sciences, National Natural Science Foundation of China and other research projects. It

aims to combine the imaging techniques and identification algorithms to enhance the users’ capacity adaptable to the

environment in the multi-modal biometric recognition system, and to accomplish iris and face recognition from the

controlled scenes to complex scenes. The group will insist on the innovation of obtaining the features of face and iris

under complex scenes. Furthermore, the group will follow the technology roadmap: “imaging devices - data resources

- recognition algorithms - integrated systems - practical applications”, and provide high-level identification technology

for public safety. Till now, the group published more than 100 international journal and conference articles and has

more than 20 granted patents. Besides, the group was awarded the 2nd Prize of National Technological Invention.

Furthermore, the biometric group has released the largest iris dataset in the world and the dataset is used by more than

10,000 research units from in more than 120 countries.

Image and Video AnalysisImage and video analysis refers to automatically analyzing objects and high-level semantic information from large-

scale images and videos based on the techniques of pattern recognition, artificial intelligence and computer vision.

The major research directions include: (1) Research on object detection and recognition in images and videos. Against

the background of big data, we research the problem of hierarchical and structural visual representation in large-scale

object detection and recognition to improve their performance in real applications. (2) Research on object tracking and

behavior analysis in wide-area and complex situations. To serve the needs of public security, semantic information of

human actions and behaviors in visual surveillance is studied to realize the abnormal behavior detection and analysis.

(3) Research on pedestrian attribute analysis in complex camera networks. This group focuses on pedestrian attribute

analysis and re-identification in non-overlapping camera networks by exploiting the relationships across cameras.

The group has published many papers in top international conferences and journals, with the second prize of National

Scientific and Technological Progress in 2011. In the PASCAL VOC challenge, the group won the first prize of object

detection in 2010 and 2011. The group also attended the ImageNet Classification and Localization with additional

training data Challenge and won the first prize of image classification in 2014.

Big Data and Multi-modal ComputingThe big data and multi-modal computing research group deals with different types of data, such as images, text, videos,

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etc. It studies both theory and applications about pattern recognition, visual computing, machine learning, data mining,

context modeling, etc. The main achievements include the champion of 2013 Internet Contest for Cloud & Mobile

Computing in human segmentation, the best student paper award of ICPR2014, etc. Major research interests of the

multi-modality computing group include: (1) Visual computing based on non-Euclidean space and large scale and multi-

modality data analysis. It studies the computational theories and mechanisms on topology-based object representations,

unsupervised clustering, cross-modality data analysis and large scale machine learning. (2) Methods and applications for

visual computing based on deep learning. It studies how to efficiently integrate the feedback mechanism in feedforward

networks and how to combine active vision in feedforward and feedback networks, which can be used to solve many

vision tasks in large scale vision analysis, e.g., object recognition, object detection, video segmentation and video

analysis. (3) Intelligent data analysis for public security and business intelligence. It studies the key technology of large

scale social network data mining taking advantage of advanced technologies of big data, such as context modeling,

time-series prediction and user modeling, which adapts to the needs of public and content securities.

Content Security and AuthenticationThe group focuses on cyber content security and authentication, by analyzing the integrality and authenticity for the

image content, based on pattern recognition and statistical analysis tools. The research of cyber content security is very

important for information forensics and security. The research topic includes: (1) Research on the statistic feature model

of image forensics. (2) Feature learning based image forensics. (3) Image tempering detection and localization based on

pattern analysis. (4) Source classification and device linking and (5) Research on prototype systems of image forensics.

Sensing and Information AcquisitionThe sensing and information acquisition group will focus on the innovation of intelligent sensing theory, methods and

equipment by the cross-fusion of artificial intelligence, cognitive science, optics, electronics and control engineering,

and provide the novel technology for National public security and information industry development. The group aims

to perceive and describe multi-modal scenes information precisely and achieve accurate reconstruction of the complex

scenes. Till now, the group focuses on the research on multi-modal biometric acquisition device, which can capture

iris, face and gait biometrics at a distance in the unconstrained environment. Such research achievement will be useful

for biometrics research and public security applications. Key technologies include light field computational imaging,

intelligent human-computer interaction, multi-biometric data preprocessing, coding, fusion, visualization and so on.

22


Research Facilities

1. DIG (Data Intelligence Gathering) platformDIG deals with big multi-modal data. It is an integrated system including data gathering, storing, managing, computing

and exhibiting based on Hadoop. It plans to explore the full potential value of big data and improve the ability of

analyzing data. The functions of DIG are described as follows.

(1) Gather, store and manage large scale multi-modal heterogeneous data and provide various interfaces for data access.

(2) Integrate many important algorithms in computer vision, pattern recognition, machine learning and data mining in

order to build a general data analysis platform.

(3) Integrate typical achievements in various scientific research areas and provide corresponding demos.

(4) Provide open and complete solutions for intelligent data analysis by taking full advantage of CRIPAC.

2. iSEE (Intelligent Scene Exploration and Evaluation) platformTaking advantage of the development of big data and related data analysis technology, iSEE integrates multi-scene

video data, large scale parallel processing architecture and intelligent video processing algorithms. Main functions of

iSEE are summarized as follows.

(1) Mass data gathering in video surveillance.

(2) Algorithm evaluation for large scale perception technologies.

(3) Large scale data mining for mass video data and large scale data retrieval for perception metadata.

(4) Multi-field knowledge integration and multi-dimension of visualization.

3. SIR (Smart Identity Recognition) platformSIR aims at exploring collaborative innovation by combining technologies of photoelectric imaging, algorithms of

image processing and theories of pattern recognition to provide human-computer interfaces with good user experience,

obtain high quality iris and face images in complex scenarios, and implement accurate, and efficient identification. It

contains three major components.

(1) An integration experimental platform developed by ourselves, which computes the optimal computational imaging

model for multi-modal biometrics in complex scenarios.

(2) A large scale biometrics database, which is the fundamental for algorithm developments, efficiency evaluation, and

standard establishment of biometrics.

(3) A library of core algorithms for biometrics, including image pre-processing, feature extraction, feature selection,

feature comparison, bioassay, and fast retrieval, secure encryption algorithms, etc.

4. Light-field cameraRaytrix is a world-famous manufacture of lenselet-based light-field camera. Raytrix can extend the depth of field up to

23


6 times compared to conventional cameras via applying the well-arranged micro-lens array with multiple focal lengths.

CRIPAC equips two types of Raytrix camera now, include:

R5: It has an approximately 5 Mega-pixels CMOS image sensor. The highest resolution of the effective image is close

to 1 Mega-pixels (25%). It can extend the depth of range up to 6 times in contrast to conventional cameras. It can be

connected to the upper computer via the GigE interface and the maximum frame rate is up to 15 fps. It can render

refocused images at 50 levels of distinguishable depths.

R25: It has an approximately 25 Mega-pixels CCD image sensor. The highest resolution of the effective image is close

to 5 Mega-pixels (25%). It can extend the depth of range up to 6 times comparing to conventional cameras. It can be

connected to the upper computer via the Camera-Link interface and the maximum frame rate is up to 25 fps. It can

render refocused images at 100 levels of distinguishable depths.

R5 R25

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Representative Research Progress

On Combining Multiple Instance Linear SVM and Bag Splitting for High Performance Visual Object Localization

Localizing objects of interest in images when provided with only image-level labels is a challenging visual recognition

task. Previous efforts have required carefully designed features and have difficulty in handling images with cluttered

backgrounds. Up-scaling to large datasets also poses a challenge to applying these methods to real applications. In

this paper, we propose an efficient and effective learning framework called MILinear, which is able to learn an object

localization model from large-scale data without using bounding box annotations. We integrate rich general prior

knowledge into a learning model using a large pre-trained convolutional network. Moreover, to reduce ambiguity in

positive images, we present a bag-splitting algorithm that iteratively generates new negative bags from positive ones.

We evaluate the proposed approach on the challenging Pascal VOC 2007 dataset, and our method outperforms other

state-of-the-art methods by a large margin; some results are even comparable to fully supervised models trained with

bounding box annotations. To further demonstrate scalability, we also present detection results on the ILSVRC 2013

detection dataset, and our method outperforms supervised DPM without using box annotations. This work has been

accepted by IEEE T-PAMI.

High-Order Topology Modeling of Visual Words for Image ClassificationModeling relationship between visual words in feature encoding is important in image classification. Recent methods

consider this relationship in either image or feature space, and most of them incorporate only pairwise relationship

(between visual words). However, in situations involving large variability in images, one cannot capture intrinsic

invariance of intra-class images using low-order pairwise relationship. The result is not robust to larger variations in

images. In addition, as the number of potential pairings grows exponentially with the number of visual words, the task of

learning becomes computationally expensive. To overcome these two limitations, we propose an efficient classification

framework that exploits high-order topology of visual words in the feature space, as follows. First, we propose a search

algorithm that seeks dependence between the visual words. This dependence is used to construct higher order topology

in the feature space. Then, the local features are encoded according to this higher order topology to improve the image

classification. Experiments involving four common data sets, namely PASCAL VOC 2007, 15 Scenes, Caltech 101,

and UIUC Sport Event, demonstrate that the dependence search significantly improves the efficiency of higher order

topological construction, and consequently increases the image classification in all these data sets. This work has been

accepted by IEEE T-IP.

Deep Recurrent Neural Networks for Visual AnalysisRecurrent neural networks have a strong ability of modelling the long-term contextual information of temporal

sequences, which are mainly used to model one-dimensional sequences before, e.g. speech and language. Recently,

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we make deep research on how to model two-dimensional visual sequences with recurrent neural networks. First,

aiming to model the spatial-temporal dependency of visual sequence and reduce the computational cost of the recurrent

connections, we propose a recurrent convolutional network which replaces the commonly-used full recurrent connection

with a weight-sharing convolutional connection. This model has been successfully used in video super-resolution, and

this work is published on Neural Information System Processing (NIPS2015). Second, considering human physical

structure and motion characteristics, we propose an end-to-end hierarchical recurrent neural network for skeleton based

action recognition. This method has high accuracy and low computational cost, and this work is published on Computer

Vision and Pattern Recognition (CVPR2015).

Research on Contextual Information ModelingContextual information modeling has wide application in data mining, user behavior modeling, and has drawn great

attention in research area. We have done thoroughly research on contextual information modeling, which achieves

progress in different aspects and meaningful results. First, we use contextual operating matrices to model contextual

effects on subjects and objects of behaviors. Meanwhile, for reducing parameter amount, we use replace contextual

matrices with a general contextual operating tensor and contextual latent vectors. Our model achieves state-of-the-

art performance in context-aware recommendation. Then, we further extend our model, and apply tensor operating

calculation for modeling multi-entity interaction, which performs well in recommendation, information retrieval

and click prediction. Furthermore, we incorporate convolutional neural networks in modeling contextual behaviors.

According to the data characteristics, we apply one-dimensional convolution and one-dimensional pooling. Our model

achieves state-of-the-art performance in click-through-rate prediction. Among our works, one has been accepted by

AAAI 2015, and two papers have been accepted by CIKM 2015, which brings extensive influence.

Representative Vector Machines: A Unified Framework for Classical ClassifiersClassifier design is a fundamental problem in pattern recognition. A variety of pattern classification methods such as the

nearest neighbor (NN) classifier, support vector machine (SVM), and sparse representation-based classification (SRC)

have been proposed in the literature. These typical and widely used classifiers were originally developed from different

theory or application motivations and they are conventionally treated as independent and specific solutions for pattern

classification. This paper proposes a novel pattern classification framework, namely, representative vector machines

(or RVMs for short). The basic idea of RVMs is to assign the class label of a test example according to its nearest

representative vector. The contributions of RVMs are twofold. On one hand, the proposed RVMs establish a unified

framework of classical classifiers because NN, SVM, and SRC can be interpreted as the special cases of RVMs with

different definitions of representative vectors. Thus, the underlying relationship among a number of classical classifiers

is revealed for better understanding of pattern classification. On the other hand, novel and advanced classifiers are

inspired in the framework of RVMs

26


Structured Ordinal Measure: Theory and MethodsIn biometrics, ordinal measure is defined as the relative ordering of some property - for example, the average brightness

of two adjacent regions or the relative ordering of two color channels within the same region. We further present a

structured ordinal measure method for biometric recognition that simultaneously learns ordinal filters and structured

ordinal features. The problem is posed as a non-convex integer program problem that includes two parts. The first part

learns stable ordinal filters to project video data into a large-margin ordinal space. The second seeks self-correcting and

discrete codes by balancing the projected data and a rank-one ordinal matrix in a structured low-rank way. Unsupervised

and supervised structures are considered for the ordinal matrix. In addition, as a complement to hierarchical structures,

deep feature representations are integrated into structured ordinal measure to enhance coding stability. An alternating

minimization method is employed to handle the discrete and low-rank constraints, yielding high-quality codes that

capture prior structures well. Experimental results on three commonly used face video databases show that our method

with a simple voting classifier can achieve state-of-the-art recognition rates using fewer features and samples.

Image Steganalysis Based on Deep LearningAn image steganlaysis scheme based on deep learning is proposed. Current steganlaysis techniques are mostly based on

heuristic feature extraction and surpervised learning. However, facing the big data era, these methods can achive good

detection results on specific and limited testing samples. Our research focuses on unsurpervised learning steganalysis

using deep learning. The steganlaysis feature can be extracted on the image neighborhood structure by the meantime for

final classification. We also design the deep learning algorithms and proper networks for final classification.

Image Steganalysis Based on Local LearningCover-Source Mismatch Problem is the most seriours problem for keeping iamge steganalysis from real application.

Facing the big data era, the exsisting steganalysis methods can achive good detection results on specific and limited

testing samples but no good for untrained or unseen samples. Our research focuses on supervised steganalysis based on

local learning, which can make best use of the relationships between cover and its corrisponding stego image pairs. The

steganlaysis feature can be extracted on the image neighborhood structure by the meantime for final classification. We

also tested our algorithms for some real application and achieve better results compared to the state-of-arts.

27


Overview of Current Research Projects

Social Sensing Data Processing for Public SecuritySources of Funding: National Fundamental Research Program

We can obtain massive data from both physical and cyber space of our society. For example, the CCTV cameras can

capture the real-time video data related to surveillance scenes, objects, human, cars, etc. And the social media such as

website, blog, micro-blog, BBS provides the thoughts and feelings of people. The social sensing data in both physical

space and cyber space is closely related to the public security. So the project aims to extract useful security-related

information from the social sensing data. The main research topics include cross-camera events understanding, cross-

media data mining of social media, and cross-space social sensing data processing.

Public Platform and Verification System for Perception Data ProcessingSource of Funding: National Fundamental Research Program

The effective processing and semantic understanding of complex perceptual data provide essential technical support

for the real-time monitoring and emergency response with respect to cyber security. This project targets on the social

perceptual data in both physical and cyber space including large-scale visual data, multimodal biometric data and large-

scale cyber unstructured data. To develop fundamental theories and specific solutions to analyze social perceptual data,

we need to: (1) address the perception mechanism and computational methods for the complex perceptual data; (2)

understand cross-scene visual data; (3) analyze cross-media cyber data; (4) collaboratively analyze data crossing the

physical and cyber space. Through these techniques the social perceptual data can be better simplified and integrated.

Also more understandable information can be utilized to forecast and respond many emergencies concerning the public

security.

Dynamic Scene Understanding across CamerasSources of Funding: National Fundamental Research Program

Generally, research on object detection, object tracking, behavior analysis and recognition is under single cameras.

However, single cameras have a limited field of views, and occlusion is always a challenging problem, greatly

influencing the system’s performance. Thus, multiple-cameras cooperate to compose a network to monitor large area,

which can partly solve the problem of occlusions. In fact, for the sake of economic cost and computational cost, non-

overlapping multi-camera tracking systems are widely used. Thus, solving the problem of non-overlapping multi-

camera object tracking is of great significance. The aim of multi-camera tracking is to establish correspondence between

observations of objects across cameras. In this project, we will focus on the object matching and recognition across

cameras, topology recovering, data association, and color transformations across cameras.

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Digital Content Service based on Copyright Protection Sources of Funding: National Key Technology Support Program

Digital copyright protection has gained more and more attention with a wide customer group in recent years. Digital

content service based on copyright protection is seemed as a useful solution for the service industry. The lack of

optimized copyright protection technology is a key issue for digital content service industry. Currently, watermarking,

encryption and digital signature authentication are commonly used in this area. Our research focused on watermarking

algorithm, copyright protection system and efficient data retrieval design etc.

Advanced Devices for Acquisition of Multi-modal Biometric Images under Unconstrained Environment

Sources of Funding: NSFC

This project aims to develop an advanced device to capture iris, face and gait biometrics at a distance under

unconstrained environment. Such a device will be useful for biometrics research, identification industry and public

security applications. The joint interdisciplinary research team has good experiences in pattern recognition, computer

vision, photoelectrical engineering, precision instruments, and integrated circuit. The device combines light field

imaging, depth perception and multispectral imaging to actively capture multi-modal biometric image sequences.

Extremely high resolution image sensors are developed from IC combination of multiple CCD or CMOS chips.

Embedded computing system will be developed for online processing, encoding, fusion and enhancement of image data.

This project will develop the first iris, face and gait image/video database under unconstrained environment and we can

use it to develop novel biometric methods and technology.

Object Detection and Classification in Image based on Visual Cognition Sources of Funding: NSFC for the Excellent Young Scholars

Object detection and classification is a fundamental and still unsolved problem. It has wide application in visual

surveillance, robot navigation and complex video understanding. The key problem in this field is how to get the

topological description of the object. Although significant progress has been achieved in this topic, it still cannot meet

people’s ultimate expectation or goal. In this project, we will address this problem from three aspects: (1) building novel

local structured feature descriptor. (2) Topologically modeling objects with local structured descriptors. (3) Reducing

model’s complexity for accelerating training and testing. Finally, we will construct a system which can be easily applied

for semantic video understanding.

Visual Computing Based on Non-Euclidean SpaceSources of Funding: NSFC

Computer vision makes machines to perceive circumstance with intelligence. It is important in both theoretical

researches and practical applications. Most current computer vision algorithms are based on Marr’s vision theory. It

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lacks robustness dealing with complex conditions and is limited to work in the Euclidean space, which is different

from the human vision system. This project originates from the study to human’s visual perception. Its major research

issues include: Mathematical basis and theoretical framework of non-Euclidean space; Visual computing and pattern

recognition of non-Euclidean space; Fusion of machine and human visual systems to explore cognitive mechanism

of non-Euclidean space; Applications of non-Euclidean space in image and video analysis, object detection and

recognition. This project attempts to change the basis of computer vision, i.e., the space it works in. It is promising to

create a new direction of computer vision and to provide theories for practical applications such as multimedia retrieve,

video surveillance and biometrics.

SteganalysisSources of Funding: NSFC, 115 Research Fund

Information hiding (Steganography) has been a hot topic and has drawn much attention in recent years. However, cases

have been reported where Steganography has been abused for bad purposes. Hence, the research of steganalysis, which

is a counter-technology of steganography aimed at detecting the presence of secret message in cover medium, serves the

urgent needs of network security to block covert communication with illegal information.

The project focuses on the following six parts. 1) Fundamental theory in security of information hiding and in

Steganalysis; 2) Study on the specific stegnography algorithms; 3) Research of pattern recognition based steganalysis

approach; 4) Research on the statistic feature model of image steganalsysis; 5) Fusion and optimization of steganalysis

feature and classifier. 6) Research on a prototype system of steganalysis which integrates the results of above research

contents.

The results of this project will enrich the theory of stegnography and steganalysis, and can give the essential foundation

of methods and techniques for reliable application of steganalysis to make a contribution for network security and

national security.

Research on Large-Scale Visual Computing Theories and MethodsSources of Funding: NSFC

Visual computing is a combination of computer vision, machine learning, pattern recognition and other related fields.

Due to the importance of ubiquitous visual data and promising applications, large-scale image / video processing and

understanding have attracted strong interest of researchers. Although in recent years there have been some important

progress, many challenging research issues remain unresolved, e.g., the limited description capacity of the existing

visual features; in terms of visual representation, large-scale multi-source heterogeneous data lack effective analysis

tools; the traditional classifier for large-scale, multi-class high-dimensional data is inefficient in visual learning and

reasoning. For a major demand of China's information industry for advanced visual computing technologies, and for

international cutting-edge research directions in the vision field, this project aims at the joint of outstanding vision

researchers from China and Australia to study large-scale visual computing theories and methods, to breakthrough

a major bottleneck in the efficiency, accuracy, robustness in terms of current visual computing technologies, and

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to develop visual feature extraction, representation, learning and reasoning methods for large-scale datasets, while

providing the theoretical basis and key technical support for a wide range of promising applications such as wide-area

video monitoring, multi-modal biometrics, and Internet mass visual information security.

Multi-modal Learning via Structured ConstraintsSources of Funding: NSFC

The integration and analysis of multi-modal data has drawn much attention in the pattern recognition and computer

vision communities. It is still a challenging and ongoing issue because the data from different modalities have different

semantic representation ability. This project makes use of the text-image pairs in web pages as an example of multi-

modal data, and focuses on the structure prior behind multi-modal data. We aim to propose new multi-modal learning

theory and methods via structure prior.

For the multi-modal theory, based on information theoretic learning and implicit regularizers, we study the mathematic

formulation of structure prior, and the measurement of correlation between multi-modal data. In particular, we study

structured sparsity and low-rank matrix constraints based on implicit regularizers, and develop a half-quadratic

framework for both of them. For multi-modal methods, we study the properties of the low-dimensional subspace of

original high-dimensional multi-modal data, and analyze the influence of different subspace structures for multi-modal

learning. And taking structure constraints as regularization terms, we further study coupled feature selection, coupled

hashing and coupled clustering for multi-modal data. Finally, we study the integration and analysis technique for multi-

modal data.

Scalable Slow Feature Learning for Intelligent Visual SurveillanceSources of Funding: NSFC

Effective feature representation is very important for developing intelligent visual surveillance applications. Current

hand-crafted features, e.g., Histogram of Oriented Gradients (HoG), has achieved superior performance in pedestrian

detection and tracking. However, it is still a hard problem to adapt the features to the variances in surveillance scenes.

In this project, we will focus on the scalable representation learning for intelligent visual surveillance. Firstly, we will

collect a large scale moving objects dataset in long-term surveillance scenes by common motion detection and tracking

methods. Then, based on slow feature analysis (SFA) which can learn invariant features from sequential data, we will

develop a scalable bottom-to-up representation learning method which is guided by the environment constraints in

surveillance scenes to obtain multi-layers slow features of moving objects in surveillance scenes. Finally, according

to different surveillance tasks, we will further optimize the generic slow features to promote the performance and

efficiency of the recognition algorithms. Through the research in this project, we will develop a whole set of scalable

slow feature learning methods for visual surveillance. These methods are highly valuable for designing the adaptive

recognition algorithms to handle the variances in surveillance scenes, so that the great progress in intelligent visual

surveillance can be achieved.

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Hierarchical Visual Attention Model Based on Deep LearningSources of Funding: NSFC

Modelling visual attention is an interdiscipline research topic which invloves in computer science, psychophysiology

and computational neuroscience. Current visual attention models mainly focus on modelling the center-surround

mechanism of the simple cells in primary visual cortex. However, large amounts of experiments show that selective

attention mechanism exists in each layer of visual cortex, and different visual features play different roles in selective

attention. In this project, we will propose a deep learning-based hierarchical visual attention model which not only

simulates the selective attention of each visual layer with its internal driving mechanism, but also learns multi-layer

visual features by combining bottom-up data and top-down task, and learns the importances of the features in each

layer. For one thing, the proposed model will improve the accuracy of fixation prediction, which has a large amount of

potential applications; for another, embedding attention mechanism into a deep learning framework will improve its

performances in several other visual tasks, such as object detection and object recognition.

Intelligent Light-field Imaging for Long-range Iris Recognition of Multiple SubjectsSources of Funding: NSFC

Iris imaging is the first essential step of iris recognition. The limitations of current iris imaging in terms of the imaging

distance, depth of field, posture and the number of users prevent wide applications of iris recognition technology.

Therefore we propose a novel iris imaging method based on light-field imaging and intelligent vision algorithms to

provide user-friendly iris image acquisition at a distance. Firstly light-field data is captured by the microlens array and

multi-sensors optical system, and then computer vision methods for human-computer interaction such as head pose

estimation, face detection and eye detection are integrated with image processing methods such as image stitching and

digital refocusing to achieve large volume iris image acquisition of multiple subjects at a distance. The innovations of

this project will establish an intelligent light-field imaging method for long-range iris recognition of multiple subjects

and promote the development of iris recognition and opto-electonic engineering technology so that long-range iris

recognition systems can be widely used in public security applications.

Content based Digital Image Forensics Sources of Funding: 115 Research Fund, NLPR

Traditionally, a photograph implies the truth of what has happened. However, in today’s digital age, sometimes seeing

is no longer believing, since our modern life is full of digital images and (maliciously) tampering these digital images is

easy and simple by using digital processing tools which are widely available (e.g., Photoshop). Many tampered images

emerge in news items, scientific experiments and even legal evidences. Therefore, we cannot take the authenticities of

images for granted any more. Image tampering detection is a branch of image forensics which aims at assessing the

authenticity and the origin of images. The tasks of image forensics can be divided into the following six categories:

source classification, device linking, processing history recovery, forgery detection and anomaly investigation. As image

tampering detection is just at its infancy stage, there is still much work to be done and some ideas can be borrowed from

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other research areas. Also, knowledge from computer vision, signal processing, computer graphics, pattern recognition

and imaging will be needed for further analysis. Our research purpose focuses on 1) Research on natural image

statistic model; 2) Learning based image splicing detection; 3) Image tempering detection and location; and 4) source

classification and device linking.

Abnormal Activity Analysis for Visual SurveillanceSources of Funding: National Fundamental Research Program

With the development of computer vision, activity analysis attracts more and more attention. Nowadays, we are

surrounded by cameras because of their low cost and high quality. It is an important application for public security and

home security to employ a computer vision method to detect and analyze abnormal activities, such as fighting, robbing

in public places, burglary. This project develops an abnormal activity analysis system facing to public places and family

scenes. This system will make alarming automatically when some violence, such as fighting and robbing, occur in

public places or some abnormal activity, such as falling down and burglary, happen in a family scene.

Light Field Camera for Advanced Computer VisionSources of Funding: Chinese Academy of Sciences

Traditional cameras transform 3D scenes into 2D images so computer vision has become an ill-posed problem. So it

is better to provide more visual information to support advanced computer vision. This project proposes to develop

a novel camera to capture 4D light field information of visual scenes. Microlens array is used to record the direction

information of light fields so that it is possible to reconstruct depth information of visual objects. In addition, image

refocusing algorithms are developed to extend the depth of field of cameras. The innovations of light field cameras will

significantly facilitate the research of computer vision. The main applications of light field cameras include iris and face

recognition at a distance, visual surveillance, etc.

Human Segmentation for Mobile Scene Understanding Sources of Funding: Global Research Outreach Program of SAMSUNG (GRO)

Human segmentation is an important step in scene understanding. In mobile scene understanding, human is probably the

most important object because most scene understanding tasks are related with humans or serve for humans. In the past

decades, the research of human segmentation is not so satisfying, i.e., not sufficiently accurate for practical applications.

In this project, we propose to explore highly accurate human segmentation based on deep learning techniques. Our

initial algorithm on a challenging human segmentation competition achieves amazing results (nearly 87% segmentation

accuracy), largely outperforming all other competitors. This approach has a potential to be a killer application of

cognitive scene understanding for mobile devices.

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Publications

International Journals1. Qiyue Yin, Shu Wu, Ran He and Liang Wang, “Multi-view Clustering via Pairwise Sparse Subspace

Representation,” Neurocomputing, vol. 156, pp.12-21, 2015.

2. Junge Zhang, Yanhu Shan and Kaiqi Huang, “ISEE Smart Home (ISH): Smart Video Analysis for Home Security,”

Neurocomputing, vol. 149, pp.752-766, 2015.

3. Jianwei Ding, Yunqi Tang, Wei Liu, Yongzhen Huang and Kaiqi Huang, “Tracking by Local Structural Manifold

Learning In A New SSIR Particle Filter,” Neurocomputing, vol. 161, pp.277-289, 2015.

4. Chong Wang and Kaiqi Huang, “How to Use Bag-of-words Model Better for Image Classification,” Image and

Visual Computing, vol. 38, pp.65-74, 2015.

5. Chong Wang and Kaiqi Huang, “VFM: Visual Feedback Model for Robust Object Recognition,” Journal of

Computer Science and Technology, vol. 30, no. 2, pp. 325-339, 2015.

6. Lijun Cao, Xu Zhang, Weiqiang Ren and Kaiqi Huang, “Large Scale Crowd Analysis Based on Convolutional

Neural Network,” Pattern Recognition, vol. 48, no.10, pp. 3016-3024, 2015.

7. Yanhu Shan, Zhang Zhang, Peipei Yang and Kaiqi Huang, “Adaptive Slice Representation for Human Action

Classification,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 25, no.10, pp. 1624-1636, 2015.

8. Zifeng Wu, Yongzhen Huang and Liang Wang, “Learning Representative Deep Features for Image Set Analysis,”

IEEE Trans. on Multimedia, vol. 17, no.11, pp. 1960-1968, 2015.

9. Yan Huang, Wei Wang and Liang Wang, “Unconstrained Multimodal Multi-Label Learning,” IEEE Trans. on

Multimedia, vol. 17, no.11, pp. 1923-1935, 2015.

10. Chong Wang, Kaiqi Huang, Weiqiang Ren, Junge Zhang and Stephen J. Maybank, “Large-Scale Weakly Supervised

Object Localization via Latent Category Learning,” IEEE Trans. on Image Processing, vol. 24, no.4, pp. 1371-1385,

2015.

11. Kaiqi Huang, Chong Wang and Dacheng Tao, “High-Order Topology Modeling of Visual Words for Image

Classification,” IEEE Trans. on Image Processing, vol. 24, no.11, pp. 3598-3608, 2015.

12. Ran He, Yingya Zhang, Zhenan Sun and Qiyue Yin, “Robust Subspace Clustering with Complex Noise,” IEEE

Trans. on Image Processing, vol. 24, no.11, pp. 4001-4013, 2015.

13. Ran He, Man Zhang, Liang Wang, Ye Ji and Qiyue Yin, “Cross-Modal Subspace Learning via Pairwise

Constraints,” IEEE Trans. on Image Processing, vol. 24, no.12, pp. 5543-5556, 2015.

14. Shu Zhang, Jian Liang, Ran He and Zhenan Sun, “Code Consistent Hashing based on Information-theoretic

Criterion,” IEEE Trans. on Big Data, vol. 1, No. 3, pp. 84-94, 2015.

15. Yanhua Cheng, Xin Zhao, Kaiqi Huang and Tieniu Tan, “Semi-supervised Learning and Feature Evaluation for

RGB-D Object Recognition,” Computer Vision and Image Understanding, vol. 139, pp. 149-160, 2015.

16. Ran He, Yinghao Cai, Tieniu Tan and Larry S. Davis, “Learning Predictable Binary Codes for Face Indexing,”

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Pattern Recognition, vol. 48, No. 10, pp. 3160-3168, 2015.

International Conferences1. Qiang Liu, Shu Wu and Liang Wang, “COT: Contextual Operating Tensor for Context-aware Recommender

Systems,” Proc. AAAI Conference on Artificial Intelligence, pp. 203-209, January 2015, Austin, USA.

2. Yong Du, Wei Wang and Liang Wang, “Hierarchical Recurrent Neural Network for Skeleton Based Action

Recognition,” Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1110-1118, June 2015,

Boston, USA.

3. Kangwei Liu, Junge Zhang, Peipei Yang and Kaiqi Huang, “GRSA: Generalized Range Swap Algorithm for the

Efficient Optimization of MRFs,” Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1761-

1769, June 2015, Boston, USA.

4. Fang Zhao, Yongzhen Huang, Liang Wang and Tieniu Tan, “Deep Semantic Ranking Based Hashing for Multi-

Label Image Retrieval,” Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1556-1564, June

2015, Boston USA.

5. Kaiye Wang, Wei Wang and Liang Wang, “Learning Unified Sparse Representations for Multi-modal Data,” Proc.

IEEE International Conference on Image Processing, pp. 3545-3549, September 2015, Quebec, Canada.

6. Yueying Kao, Chong Wang and Kaiqi Huang, “Visual Aesthetic Quality Assessment with a Regression Model,”

Proc. IEEE International Conference on Image Processing, pp. 1583-1587, September 2015, Quebec, Canada.

7. Jingyu Liu, Yongzhen Huang, Xiaojiang Peng and Liang Wang, “Multi-view Descriptor Mining via Codeword Net

for Action Recognition,” Proc. IEEE International Conference on Image Processing, pp. 793-797, September 2015,

Quebec, Canada.

8. Lianrui Fu, Junge Zhang and Kaiqi Huang, “Context Aware Model for Articulated Human Pose Estimation,” Proc.

IEEE International Conference on Image Processing, pp. 991-995, September 2015, Quebec, Canada.

9. Yuqi Zhang, Wei Wang and Liang Wang, “Scene Text Recognition with Deeper Convolutional Neural Networks,”

Proc. IEEE International Conference on Image Processing, pp. 2384-2388, September 2015, Quebec, Canada.

10. Qiyue Yin, Shu Wu and Liang Wang, “Partially Tagged Image Clustering,” Proc. IEEE International Conference on

Image Processing, pp. 4012-4016, September 2015, Quebec, Canada.

11. Jinde Liu, Xin Zhao, Kaiqi Huang and Tieniu Tan, “Learning Occlusion Patterns Using Semantic Phrases for Object

Detection,” Proc. IEEE International Conference on Image Processing, pp. 686-690, September 2015, Quebec,

Canada.

12. Xikai Xu, Jing Dong, Wei Wang and Tieniu Tan, “Robust Steganalysis Based on Training Set Construction and

Ensemble Classifiers Weighting,” Proc. IEEE International Conference on Image Processing, pp. 1498-1502,

September 2015, Quebec, Canada.

13. Fei Liu, Guangqi Hou, Zhenan Sun and Tieniu Tan, “Albedo Assisted High-Quality Shape Recovery from 4D

Light Fields,” Proc. IEEE International Conference on Image Processing, pp. 1220-1224, September 2015, Quebec,

Canada.

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14. Xikai Xu, Jing Dong, Wei Wang and Tieniu Tan, “Local Correlation Pattern for Image Steganalysis,” Proc. IEEE

China Summit and International Conference on Signal and Information Processing, pp. 468-472, July 2015,

Chengdu, China.

15. Bo Peng, Wei Wang, Jing Dong and Tieniu Tan, “Detection of Computer Generated Faces in Videos Based on Pulse

Signal,” Proc. IEEE China Summit and International Conference on Signal and Information Processing, pp. 841-

845, July 2015, Chengdu, China.

16. Kaiye Wang, Wei Wang, Liang Wang and Ran He, “A Two-step Approach to Cross-modal Hashing,” Proc. ACM

International Conference on Multimedia Retrieval (ICMR, Short Paper), pp. 459-462, June 2015, Shanghai, China.

17. Song Xu, Shu Wu and Liang Wang, “Personalized Semantic Ranking for Collaborative Recommendation,” Proc.

ACM SIGIR Conference on Research and Development in Information Retrieval pp. 971-974, August 2015,

Santiago, Chile.

18. Qiang Liu, Shu Wu and Liang Wang, “Collaborative Prediction for Multi-entity Interaction with Hierarchical

Representation,” Proc. ACM International Conference on Information and Knowledge Management, pp. 613-622,

October 2015, Melbourne, Australia.

19. Qiang Liu, Feng Yu, Shu Wu and Liang Wang, “A Convolutional Click Prediction Model,” Proc. ACM International

Conference on Information and Knowledge Management, pp. 1743-1746, October 2015, Melbourne, Australia.

20. Qiyue Yin, Shu Wu and Liang Wang, “Incomplete Multi-view Clustering via Subspace Learning,” Proc. ACM

International Conference on Information and Knowledge Management, pp. 383-392, October 2015, Melbourne,

Australia.

21. Weiyu Guo, Shu Wu, Liang Wang and Tieniu Tan, “Social-Relational Topic Model for Social Networks,” Proc.

ACM International Conference on Information and Knowledge Management, pp. 1731-1734, October 2015,

Melbourne, Australia.

22. Dong Wang, Qiyue Yin, Ran He, Liang Wang and Tieniu Tan, “Multi-view Clustering via Structured Low-Rank

Representation,” Proc. ACM International Conference on Information and Knowledge Management, pp. 1911-1914,

October 2015, Melbourne, Australia.

23. Di Miao, Man Zhang, Haiqing Li, Zhenan Sun and Tieniu Tan, “Bin-based Weak Classifier Fusion of Iris and

Face Biometrics,” Proc. IEEE International Conference on Biometrics: Theory, Applications and Systems, pp. 1-6,

September 2015, Washington, USA.

24. Yasser Chacon-Cabrera, Man Zhang, Eduardo Garea Llano and Zhenan Sun, “Iris Texture Description Using

Ordinal Co-occurrence Matrix Features,” Proc. Iberoamerican Congress on Pattern Recognition, pp. 184-191,

November 2015, Montevideo, Uruguay.

25. Lingxiao Song, Man Zhang, Zhenan Sun, Jian Liang and Ran He, “Two-Step Greedy Subspace Clustering,” Proc.

Pacific-Rim Conference on Multimedia, pp. 45-54, September 2015, Gwangju, Korea.

26. Feng Liu, Yongzhen Huang, Wankou Yang and Changyin Sun, “High-level Spatial Modeling in Convolutional

Neural Network with Application to Pedestrian Detection,” Proc. IEEE Canadian Conference on Electrical and

Computer Engineering, pp. 778-783, May 2015, Halifax, Nova Scotia, Canada.

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27. Qiyue Yin, Shu Wu and Liang Wang, “Learning to Hash for Recommendation with Tensor Data,” Proc. Asia Pacific

Web Conference, pp. 292-303, September 2015, Guangzhou, China.

28. Weiyu Guo, Shu Wu, Liang Wang and Tieniu Tan, “Multiple Attribute Aware Personalized Ranking,” Proc. Asia

Pacific Web Conference, pp. 244-255, September 2015, Guangzhou, China.

29. Yanhua Cheng, Rui Cai, Xi Zhao and Kaiqi Huang, “Convolutional Fisher Kernels for RGB-D Object Recognition,”

Proc. International Conference on 3D Vision, pp. 135-143, October 2015, France.

30. Yung-Hsiang Lu, Alan M. Kadin, Alexander C. Berg, Thomas M. Conte, Erik P. DeBenedictis, Rachit Garg, Ganesh

Gingade, Bichlien Hoang, Yongzhen Huang, Boxun Li, Jingyu Liu, Wei Liu, Huizi Mao, Junran Peng, Tianqi

Tang, Elie K. Track, Jingqiu Wang, Tao Wang, Yu Wang, Jun Yao, “Rebooting Computing and Low-Power Image

Recognition Challenge,” Proc. International Conference on Computer Aided Design, pp. 927-932, November 2015,

Austin, America.

31. Chi Zhang, Zhiwei Li, Yanhua Cheng, Rui Cai, Yanghong Chao and Yong Rui, “MeshStereo: A Global Stereo

Model with Mesh Alignment Regularization for View Interpolation,” Proc. IEEE International Conference on

Computer Vision, pp. 2057-2065, December 2015, Santiago, Chile.

32. Yanhua Cheng, Rui Cai, Chi Zhang, Zhiwei Li, Xin Zhao, Kaiqi Huang and Yong Rui, “Query Adaptive Similarity

Measure for RGB-D Object Recognition,” Proc. International Conference on Computer Vision, pp. 145-153,

December 2015, Santiago, Chile.

33. Chunshui Cao, Xianming Liu, Jiang Wang, Yinan Yu, Wei Xu, Yi Yang, Deva Ramanan, Chang Huang, Zilei Wang,

Thomas Huang, Yongzhen Huang, and Liang Wang, “Look and Think Twice: Capturing Top-down Visual Attention

With Feedback Convolutional Neural Networks,” Proc. International Conference on Computer Vision, pp. 2956-

2964, December 2015, Santiago, Chile.

34. Yan Huang, Wei Wang and Liang Wang, “Conditional High-order Boltzmann Machine: A Supervised Learning

Model for Relation Learning,” Proc. IEEE International Conference on Computer Vision, pp. 4265-4273, December

2015, Santiago, Chile.

35. Lianrui Fu, Junge Zhang and Kaiqi Huang, “Beyond Tree Structure Models: A New Occlusion Aware Graphical

Model for Human Pose Estimation,” Proc. IEEE International Conference on Computer Vision, pp. 1976-1984,

December 2015, Santiago, Chile.

36. Lianrui Fu, Junge Zhang and Kaiqi Huang, “Mirrored Non-Maximum Sup-pression for Accurate Object Part

Localization,” Proc. Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur, Malaysia.

37. Dangwei Li, Xiaotang Chen and Kaiqi Huang, “Multi-attribute Learning for Pedestrian Attribute Recognition

in Surveillance Scenarios,” Proc. Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur,

Malaysia.

38. Chunfeng Song, Yongzhen Huang, Zhenyu Wang and Liang Wang, “1000fps Human Segmentation with Deep

Convolutional Neural Networks,” Proc. Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur,

Malaysia.

39. Kaihao Zhang, Yongzhen Huang, Hong Wu and Liang Wang, “Facial Smile Detection Based on Deep Learning

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Features,” Proc. Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur, Malaysia.

40. Hongsong Wang, Wei Wang and Liang Wang, “Hierarchical Motion Evolution for Action Recognition,” Proc. Asian

Conference on Pattern Recognition, November 2015, Kuala Lumpur, Malaysia.

41. Yong Du, Yun Fu and Liang Wang, “Skeleton Based Action Recognition with Convolutional Neural Network,”

Proc. Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur, Malaysia.

42. Lingxiao Song, Man Zhang, Qi Li, Zhenan Sun and Ran He, “Float Greedy-search-based Subspace Clustering,”

Proc. Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur, Malaysia .

43. Pengcheng Liu, Peipei Yang, Kaiqi Huang and Tieniu Tan, “Uniform Low-Rank Representation for Unsupervised

Visual Domain Adaptation,” Proc. Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur,

Malaysia.

44. Dong Wang, Ran He, Liang Wang and Tieniu Tan, “Adaptive Multi-view Clustering via Cross Trace Lasso,” Proc.

Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur, Malaysia.

45. Dong Cao, Ran He, Zhenan Sun and Tieniu Tan, “Joint Space Learning for Video-based Face Recognition,” Proc.

Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur, Malaysia .

46. Shu Zhang, Man Zhang, Qi Li, Tieniu Tan and Ran He, “Supervised Topology Preserving Hashing,” Proc. Asian

Conference on Pattern Recognition, November 2015, Kuala Lumpur, Malaysia.

47. Jian Liang, Dong Cao, Ran He, Zhenan Sun and Tieniu Tan, “Principal Affinity based Cross-Modal Retrieval,”

Proc. Asian Conference on Pattern Recognition, November 2015, Kuala Lumpur, Malaysia .

48. Yan Huang, Wei Wang, Liang Wang, “Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-

Resolution,” Proc. Advances in Neural Information Processing Systems, pp. 235-243, December 2015, Quebec,

Canada.

49. Dong Cao, Ran He, Man Zhang, Zhenan Sun and Tieniu Tan, “Real-world Gender Recognition Using Multi-Order

LBP and Localized Multi-boost Learning,” Proc. IEEE International Conference on Identity, Security and Behavior

Analysis, pp. 1-6, March 2015, Hong Kong.

50. Weiyu Guo, Shu Wu, Liang Wang and Tieniu Tan, “Adaptive Pairwise Learning for Personalized Ranking with

Content and Implicit Feedback,” Proc. IEEE/WIC International Conference on Web Intelligence, December 2015,

Singapore.

51. Bo Peng, Wei Wang, Jing Dong and Tieniu Tan, “Improved 3D Lighting Environment Estimation for Image Forgery

Detection,” Proc. IEEE International Workshop on Information Forensics and Security, November 2015, Rome,

Italy.

52. Kaihao Zhang, Yongzhen Huang, Hong Wu and Liang Wang, “Kinship Verification with Deep Convolutional Neural

Networks,” Proc. British Machine Vision Conference, September 2015, Swansea, UK.

53. Pengcheng Liu, Chong Wang, Peipei Yang, Kaiqi Huang and Tieniu Tan, “Cross-Domain Object Recognition Using

Object Alignment,” Proc. British Machine Vision Conference (BMVC), September 2015, Swansea, UK.

54. Guangqi Hou, Fei Liu and Zhenan Sun, “Computer Vision Research with New Imaging Technology,” Proc. SPIE

International Symposium on Multispectral Image Processing and Pattern Recognition, October 2015, Enshi, China.

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55. Xiang Wang, Lin Li and Guangqi Hou, “Super-Resolved All-Refocused Image with A Plenoptic Camera,” Proc.

SPIE International Symposium on Multispectral Image Processing and Pattern Recognition, October 2015, Enshi,

China.

56. Xikai Xu, Jing Dong, Wei Wang and Tieniu Tan, “An Adaptive Ensemble Classifier for Steganalysis Based on

Dynamic Weighted Fusion,” Proc. International Conference on Communications, Signal Processing, and Systems

(CSPS), October 2015, Chengdu, China.

57. Yinlong Qian, Jing Dong, Wei Wang and Tieniu Tan, “Learning Representations for Steganalysis from Regularized

CNN Model with Auxiliary Tasks,” Proc. SPIE Media Watermarking, Security, and Forensics, October 2015,

Chengdu, China.

58. Yinlong Qian, Jing Dong, Wei Wang and Tieniu Tan, “Deep Learning for Steganalysis Via Convolutional Neural

Networks,” Proc. SPIE Media Watermarking, Security, and Forensics, pp. 94090J-1- 94090J-10, March 2015, San

Francisco, United States.

National Conferences1. Wei Tang, Yongzhen Huang and Liang Wang, “1000 fps Highly Accurate Eye Detection with Stacked Denoising

Autoencoder,” Proc. Chinese Conference on Computer Vision, pp. 237-246, September 2015, Chengdu, China.

2. Qi Zhang, Haiqing Li, Man Zhang, Zhaofeng He, Zhenan Sun and Tieniu Tan, “Fusion of Face and Iris Biometrics

on Mobile Devices Using Near-infrared Images,” Proc. Chinese Conference on Biometric Recognition, pp. 569-

578, November 2015, Tianjin, China.

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Awards and Recognitions

Young top-notch talent for Ten Thousand Talent Program, 2015: Kaiqi Huang

Excellent member of CAS Youth Innovation Promotion Association, 2015: Kaiqi Huang

National Science Fund for Distinguished Young Scholars, 2015: Liang Wang

Excellent in the final assessment for CAS Hundred Talent Program, 2015: Liang Wang

Best student paper award in CCCV2015: Wei Tang (advisor: Liang Wang)

Best paper award in CCBR2015: Qi Zhang (advisor: Tieniu Tan)

Best paper award in CSPS 2015: Xikai Xu (advisor: Tieniu Tan)

Member of CAS Youth Innovation Promotion Association, 2015: Ran He

Second Prize of Wu Wenjun Artificial Intelligence Science and Technology Innovation Award, 2015: Ran He

ACM Beijing rising star award, 2015: Ran He

Tencent Rhino Bird Excellent Prize, 2015: Yongzhen Huang

Second Prize of Low Power Image Recognition Challenge and Prize of Highest Accuracy with Low Energy in

Design Automation Conference, 2015: Yongzhen Huang

Champion of “Digital Cup” Basketball League of Institute of Automation, Chinese Academy of Sciences, 2015:

Center for Research on Intelligent Perception and Computing (CRIPAC)

40


Professional Activities

Jing Dong

Secretary General Deputy of Chinese Association for Artificial Intelligence (2014-now)

Member of CCF Computer Vision Task Forces (2014-now)

Council Member, Beijing Society of Image and Graphics (2012-now)

Chair of Student Activities Committee of IEEE Beijing Section (2010-now)

IEEE MGA Committee member (2016)

Ran He

Member of Editorial Board, IET image processing (2012-)

Council Member, Beijing Society of Image and Graphics (2012-)

Member of Editorial Board, Neurocomputing ( 2011-)

Kaiqi Huang

Guest Ediotr of IEEE Trans on Systems, Man, and Cybernetics: Systems Special issue on Large Scale Video

Analytics for Enhanced Security: Algorithms and Systems, 2015

Co-Chair of the International Workshop on Visual Surveillance and Re-Rdentification (Vs-ReID) conjuncted

with European Conference on Computer Vision (ECCV2014) Sep.12, 2014.

Associate Editor of IEEE Trans on Systems, Man, and Cybernetics: Systems (TSMC) (2014-)

Associate Editor of Electronic Letters on Computer Vision and Image Analysis (ELCVIA) (2010-)

Board Member, IEEE SMC Technical Committee on Cognitive Computing (2008-)

Associate Editor of International Journal of Image and Graphics (IJIG) (2007-)

Yongzhen Huang

Associate Editor, Neurocomputing, 2014 -2016, ISSN: 0925-2312

Zhenan Sun

Secretary General of the Pattern Recognition Committee of Chinese Association for Artificial Intelligence(CAAI)

(2014-)

Editorial Board of IEEE Transactions on Information Forensics and Security (2013 -)

Secretary General of the Chinese Biometrics Alliance(CBA) (2012 -)

Editorial Board of IEEE Biometrics Compendium (2012 -)

Secretary General, IAPR Technical Committee on Biometrics (2006 -)

41


Tieniu Tan

President of Beijing Society of Image and Graphics (2012-)

Deputy President of the Chinese Association for Artificial Intelligence (2010 -)

Member of Editorial Board, IET Image Processing(2008 -)

Member of Advisory Board, Journal of Real-Time Image Processing(2006 -)

Editor-in-Chief , Inter. J. Automation and Computing (2004 -)

Liang Wang

Associate editor of IEEE Transactions on Information Forensics and Security (IEEE TIFS) (2013 -)

Associate editor, IEEE Transactions on Cybernetics (IEEE TSMC-B) (2007 -)

42


Academic Exchanges and Cooperations

Conferences and Workshops1. 2nd Forum on Big Image and Video Data organized by CRIPAC was held in Beijing on April 7.

2. The Brain-like Intelligence Innovation forum organized by Tianjin Academy for Intelligent Recognition

Technologies (TAfIRT) was held in Tianjin on June 27.

3. The 2015 Face Recognition and Application Symposium organized by CRIPAC was held in Institute of

Automation, Chinese Academy of Sciences on August 7.

4. 3rd Forum on Big Image and Video Data organized by CRIPAC was held in Beijing on October 20.

5. 4th Forum on Chinese Biometrics Industry was held in Shenzhen on October 30. It was hosted by the Chinese

Biometrics Alliance (CBA).

6. 10th Chinese Conference on Biometric Recognition (CCBR2015) was held in Tianjin from November 13 to 15

in 2015. It was hosted by the Tianjin Academy for Intelligent Recognition Technologies (TAfIRT) and Chinese

Biometrics Alliance (CBA).

Visits External VisitsQiang Liu

January 24-30, 2015, AAAI Conference on Artificial Intellegence (AAAI2015) in Austin, USA.

Kaiqi Huang

From Feb 6, 2015 to Feb 5, 2016, visiting researcher in Oxford University, supported by China Scholarship

Council.

Man Zhang

March 23-25, 2015, IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015) in

Hong Kong.

Wei Wang, Yong Du

June 6-13, 2015, the 28th IEEE International Conference on Computer Vision and Pattern Recognition

(CVPR2015) in Boston, USA.

Zhenan Sun

June 21–26, 2015, the 12th Biometrics Summer School in Alghero, Italy.

Di Miao

September 7-12, 2015, the 7th IEEE International Conference on Biometrics: Theory, Applications and Systems

(BTAS2015) in Washington D.C., USA.

Jing Dong, Fei Liu, Lianrui Fu

September 26 - October 2, 2015, the 22nd International Conference on Image Processing (ICIP2015) in Quebec

43


City, Canada.

Shu Wu, Qiyue Yin

October 19-23, 2015, ACM International Conference on Information and Knowledge Management (CIKM2015)

in Melbourne, Australia.

Bo Peng

November 15-20, 2015, the 7th International Workshop on Information Forensics and Security (WIFS2015) in

Rome, Italy.

Weiyu Guo

December 6-9, 2015, IEEE/WIC/ACM International Conference on Web Intelligence (WI2015), Singapore.

Tieniu Tan

November 03-04, 2015, the 3rd Asian Conference on Pattern Recognition (ACPR2015) in Kuala Lumpur, Malaysia.

Liang wang, Lingxiao Song, Hongsong Wang

November 02-07, 2015, the 3rd Asian Conference on Pattern Recognition (ACPR2015) in Kuala Lumpur, Malaysia.

Wei Wang, Yan Huang

December 6-14, 2015, Advances in Neural Information Processing Systems (NIPS2015) in Quebec, Canada.

Yanhua Cheng, Chunshui Cao

December 9-20, 2015, IEEE International Conference on Computer Vision (ICCV2015) in Santiago, Chile.

Visits to CRIPACFrom May to August in 2015, Prof. Edel Bartolo Garcia Reyes from Advanced Technologies Application Center

of Cuba visited CRIPAC.

From July 1 to July 30 in 2015, Associate Prof. Rocio Gonzalez-Diaz from University of Seville visited CRIPAC.

44


Research Talks by External Visitors

Time Speaker Title

March 5Postdoctoral Researcher Xiaojiang PengInstitut national de recherche en informatique et en automatique

Video-Based Human Action Analysis

March 13 Associate Prof. Mingming ChengNankai University Efficient Image Scene Analysis and Applications

April 10 Lead Researcher Jingdong WangMicrosoft Research Asia Big Media Data: Search and Management

July 7 Associate Prof. Zhuowen TuUniversity of California

Deep Supervision for Deep Learning: Training, Regularization, and Multi-Scale Learning

July 30 Prof. Yi ZengInstitute of Automation, CAS

Brain-inspired Intelligence: Opportunities, Progresses, and Grand Challenges

July 30 Associate Prof. Yongjin LiuTsinghua University

Cognitive Mechanism Related to Line Drawings and Its Applications in Intelligent Processing of Visual Media

July 30 Prof. Rongrong Ni Beijing Jiaotong University Recent Advances in Digital Image Forensics

July 30 Lead Researcher Jingdong WangMicrosoft Research Asia Salient Object Detection and Its Applications

July 30 Prof. Peng Cui Tsinghua University Social-sensed Multimedia Computing

July 30 Prof. Xinshan Zhu Tianjin University Small Unmanned Aerial Vehicle Theory and Applications

45


Memorabilia in 2015

1. 2nd Forum on Big Image and Video Data organized by CRIPAC was successfully held in Beijing on April 7. More

than 300 people from government, industry, academia, investment and media were present.

2. Tianjin Academy for Intelligent Recognition Technologies (TAfIRT) was officially established on April 29.

Members in CRIPAC will jointly push the developments of applying intelligent recognition technologies to identity

authentication, video surveillance, human computer interaction, information retrieval.

3. On June 17, Prof. Tieniu Tan was invited to give a talk on “the present and future for Artificial Intelligence” during

the 8th group study of the 12th Standing committee of the National Committee of the CPPCC. After the talk, Prof.

Tieniu Tan had a discussion with the committee members.

4. The Brain-like Intelligence Innovation forum organized by Tianjin Academy for Intelligent Recognition

Technologies (TAfIRT) was held in Tianjin on June 27. More than 200 experts and representatives from both home

and abroad attended the forum.

5. On July 8, our basketball team won the “Digital Cup” Basketball League of Institute of Automation, Chinese

Academy of Sciences, 2015.

6. Summer Symposium of CRIPAC was successfully held from July 17 to July 19 in 2015.

7. On August 7, The 2015 Face Recognition and Application Symposium organized by CRIPAC was held in Institute

of Automation, Chinese Academy of Sciences. More than 200 people were present.

8. CRIPAC organized an Orientation Day for new students On September 14.

9. 3rd Forum on Big Image and Video Data organized by CRIPAC was successfully held in Beijing on October 20.

More than 300 people from government, industry, and academia were present.

10. 4th Forum on Chinese Biometrics Industry organized by Chinese Biometrics Alliance (CBA) was held in Shenzhen

on October 30. More than 200 people were present.

11. 10th Chinese Conference on Biometric Recognition (CCBR2015) was held in Tianjin from November 13 to 15

in 2015. It was hosted by the Tianjin Academy for Intelligent Recognition Technologies (TAfIRT) and Chinese

Biometrics Alliance (CBA). More than 280 people were present.

12. The annual meeting of the 973 project led by Prof. Tieniu Tan, was held on December 27.

13. The annual Winter Symposium of CRIPAC was successfully held from December 26 to December 28 in 2015.

Documents

Message from Directorcripac.ia.ac.cn/en/attached/file/20160824/20160824150429_752.pdfDenoising Autoencoder” by PhD student Wei Tang won the Best student Paper Award of CCCV2015,