Soft Biometrics at CUBS Venu Govindaraju CUBS, University at Buffalo govind@

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Slide 2 Soft Biometrics at CUBS Venu Govindaraju CUBS, University at Buffalo Slide 3 Background Traits of biometrics Universality Distinctiveness Permanence Collectability Acceptability Present perfect? No biometric is truly universal. It is estimated that 2- 4% of the population have unusable fingerprints Each biometric has a lower bound for errors (constraint of algorithm + individuality) Individual biometrics need to be augmented by other biometrics (multi-modal) or traits (soft biometrics) Slide 4 Soft Biometrics Not very distinctive Can be used to augment regular biometrics Not typically used during verification/identification More intuitive than strong biometrics Definition[1] Soft biometric traits are those characteristics that provide some information about the individual but are not distinctive enough to sufficiently differentiate any two individuals [1] [1] A. K. Jain, S. Dass, K. Nandakumar, Soft Biometrics for Personal Identification, SPIE Defense and Security Symposium 2003 Slide 5 Soft Biometrics : Examples Other classification Continuous: Age, Height, Weight etc. Discrete: Gender, Eye Color, Ethnicity etc. Slide 6 Motivation Heckathorn[3] have shown that a combination of personal attributes can be used to identify the individual reliably Binning and Indexing Hardening primary biometric Speech Recognition Can be used to tune individual biometrics Socially aware computing (call centers)? Slide 7 Extracting Soft Biometric Traits Devices Color video Stereo images Challenges Controlled vs Uncontrolled environment Pose variations Illumination variation Complex backgrounds Feature selection and extraction Features used in traditional biometrics do not encode soft biometric traits Decision systems (soft thresholds) Slide 8 Problems in Representation Purely statistical features Fuzzy class boundaries Slide 9 Soft Biometrics Research at CUBS Speech Gender Identification Accent Identification Face Face Catalog: Semantic Face Retrieval Gender Classification Skin Skin spectroscopy Slide 10 Soft Biometric Traits in Speech Gender There exists a difference in the pitch period between genders This difference is fundamental in the discrimination between males and females Accent [1] Temporal features: onset time, closure/voicing/word duration Prosodic/Intonation slope patterns Formant frequencies Age The average power measurement and speech rate are used as indicators for measurement of agedness in a speaker [1]A Study of Temporal Features and Frequency characteristics in American English Foreign Accent L.M. Arslan, J.H.L. Hansen, Journal of the Acoustical society of America, July 1997 Slide 11 Uses of Soft Biometrics in Speech Soft Biometrics for binning Primary Biometric Soft Biometric(s) P(w|x 1 )P(w|x 1 y) Soft Biometrics for improving accuracy Slide 12 Loose Gender Classification (PITCH) 3 Methods Fast Fourier Transform Linear Predictive Analysis Cepstral Analysis Data 75 files Males -41, Females -34 Male LowMale Medium Male High Female Low Female Medium Female High 132Hz 156Hz 171Hz 205Hz 230Hz 287Hz Results Slide 13 Definition of Accent (linguistics) An accent is the perceived peculiarities of pronunciation and intonation of a speaker or group of speakers A foreign accent is defined in a way that the phonology of the spoken language is modified by the phonology of another language, more familiar to the speaker 3 major language groups American Chinese Indian Slide 14 Proposed Approach for Accent First identify the accent markers Determine the effect of gender and co-articulation Initially develop a text dependent model Accumulate evidence over time Features: formants phoneme duration instantaneous (mel)cepstral slopes HMMs Slide 15 Accent Markers A look at various non-native pronunciations of English CHINESE r read sometimes as l or w v read as w th read as d n and l often confused Often drop articles like the and a INDIAN SUBCONTINENT Use of the rhotic r Use of rolling l Fast speech tempo with choppy syllables Rhythmic variation of pitch Websters Revised Unabridged Dictionary Definition of non-native pronunciations of English Slide 16 F2 F3 PLEASESTELLA SLABSPLASTIC MALES PHONEME CONTAINING L American - Indian - Slide 17 F3 F2 BRING F3 F2 RED F3 F2 FRESH MALES PHONEMES CONTAINING R AND AA F2 F3 ASK American - Indian - Slide 18 FEMALES SEGMENTED PHONEMES L, R, AA F3 F2 PLEASE F3 F2 STELLA F3 F2 RED F3 F2 ASK American - Indian - Slide 19 Soft Biometrics for Law Enforcement Novel Forensic System Slide 20 Law Enforcement Application: Face Catalog User can select some facial feature to describe. System will prompt the user after each query with the best feature for the next query. Slide 21 Related Work Identikit [1] composes faces by putting together transparencies of facial features. Evofit [2], automate the process of identikits. Phanthomas [3] face composition using elastic graph matching. CAFIIRIS [4] and Photobook [5] use PCA for face composition and matching. But general description of users are semantic! 1.V. Bruce, Recognizing Faces, Faces as Patterns, pp. 37-58, Lawrence Earlbaum Associates, 1988 2.Frowd, C.D., Hancock, P.J.B., & Carson, D. (2004). EvoFIT: A Holistic, Evolutionary Facial Imaging Technique for Creating Composites, ACM TAP, Vol. 1 (1) 3.Phantomas: Elaborate Face Recognition .Product description: 4.J. K. Wu, Y. H. Ang, P. C. Lam, S. K. Moorthy, A. D. Narasimhalu, Facial Image Retrieval, Identification, and Inference System 5.A. Pentland, R. Picard, S. Sclaroff, Photobook: tools for content based manipulation of image databases, Proc. SPIE: Storage and Retrieval for Image and Video Databases II, vol. 2185 Slide 22 Face Catalog System Overview Face Detection Lip Location and parameterization Eye Location Parameterization of other Features Query Sub-System Prompting Sub-System Semantic Description Face Image Database Meta Database Input Image Sorted Images user Semantic Face Retrieval System Slide 23 Enrollment Sub-System Face Detection. Lips and eye detection. Locate and parameterize other features. Slide 24 Query Sub-System Pruning images based on descriptions given? What if user makes a mistake in one of the description. Ranking images based on their probability of being the required person is a better idea. Bayesian learning can be used to update probability of each face being the required one. Prompting users the feature with highest entropy at each step. Slide 25 Example Query Query = [] Query = [Spectacles = Yes] Query = [Spectacles = Yes + Mustache = Yes] Query = [Spectacles = Yes + Mustache = Yes + Nose = Big] Probabilities of Faces Slide 26 Results Results of Enrollment Sub-system (Database of 150 images) Results of Query (25 users, 125 test cases) Top 5Top 10Top 15 Average no. of queries. 7.12 5.082.49 FeaturesNumber of False Accepts Number of False Rejects Spectacles 12 Mustache 24 Beard 40 Long Hair 28 Balding 10 Slide 27 Gender Classification in Images Gender classification Identifying male or female from facial image Existing approaches Geometric feature based [1]-[2] Appearance feature based (raw data feature or PCA + classifier) [3] Approaches using other features, e.g., wrinkle and skin color [4] [1] A. Burton, V. Bruce and N. Dench, Whats the difference between men and women? Evidence from facial measurements, Perception, vol. 22, pp.153-176, 1993. [2]R. Brunelli and T. Poggio, Hyperbf network for gender classification, DARPA Image Understanding Workshop, pp. 311-314, 1992. [3]B.A. Golomb, D.T. Lawrence, T.J. Sejnowski, Sexnet: A Neural Network Identifies Sex from Human Faces, Advances in Neural Information Processing Systems3, R.P Lippmann, J.E. Moody, D.S. Touretzky, eds. Pp. 572-577, 1991. [4] J. Hayashi, M. Yasumoto, H. Ito, H. Koshimizu, Age and gender estimation based on wrinkle texture and color of facial images,, Proceedings of 16th International Conference on Pattern Recognition, vol. 1, pp. 405 - 408, 11-15 Aug. 2002 Slide 28 Gabor Feature based gender classification system Feature Extractor Using Gabor Wavelet SVM Classifier Preprocessing (Face detection, normalization, etc.) Raw Image Decision Slide 29 Facial image Normalization Mapping feature points to fixed positions Feature points Centers of two pupils Tip of the nose Normalized image 64 by 64 Convert from color to grayscale by averaging RGB components Slide 30 Gabor feature Gabor filter and Gabor wavelet [B.S. Manjunath, et al, PAMI, 1996] Gabor Filter: Gabor Wavelet: Fourier Transform of g(x, y): Slide 31 Redundancy reduction [B.S. Manjunath, et al, PAMI, 1996] Let and denote the lowest and highest frequencies of interest are determined by Gabor feature (cont.) Slide 32 Characteristics of Gabor wavelet A powerful tool to capture changes of signals Selective on certain frequency and orientation by setting parameters m, n Gabor feature for gender classification Gabor WT at 4 scalses, 4 orientations (m = 0,.., 3; n = 0, , 3) Each output image of Gabor WT (64 by 64) is divided into non- overlapping blocks of the size 2 m+2 by 2 m+2 (m: the scale number). Average of magnitudes in each block as a feature Total number of features Slide 33 Gabor feature (cont.) Slide 34 Classification Features 1360-dimensional training and testing vectors fed into SVM classifier Classifier SVM with Gaussian RBF kernel [6] (B. Moghaddam, et al, PAMI 2002) Adjust to minimize error rate 1360 features from Gabor WT (in 4 scales, 4 orientations) of 6464 input image Training and testing vectors (of 1360 dimensions) normalized into unit vectors Slide 35 Experimental Results Dataset: AR face database [A.M. Martinez and R. Benavente, The AR face database, CVC Tech. Report #24, 1998] Overall: 3265 frontal facial images inc