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Fusion of Face and Iris Biometrics from a Stand-Off Video Sensor. Ryan Connaughton Kevin W. Bowyer Patrick Flynn April 16, 2011 Computer Vision Research Lab Department of Computer Science & Engineering University of Notre Dame. Biometrics and Multi-Biometrics. Biometric Trait. Biometric - PowerPoint PPT Presentation
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Fusion of Face and Iris Biometrics from a Stand-Off
Video Sensor
Ryan ConnaughtonKevin W. Bowyer
Patrick Flynn
April 16, 2011Computer Vision Research Lab
Department of Computer Science & EngineeringUniversity of Notre Dame
Biometrics and Multi-Biometrics
BiometricTrait
Sensor MatcherBiometricSample Output
Multi-Modal Multi-Sensor
Multi-Sample
Multi-Algorithm
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Redundancy at any stage is referred to as multi-biometrics
Fusion in Multi-Biometrics
Fusion: Combining information from multiple sources
Types of fusion:
– Signal Level
– Feature Level
– Score Level
– Rank Level
– Decision Level
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Advantages and Disadvantages
Potential advantages of multi-biometrics:
– Increased recognition accuracy
– Wider population coverage & lower failure-to-acquire rates
– More difficult to spoof
Potential disadvantages:
– Increased computation time
– Increased acquisition time
– Increased sensor cost
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Project Goal
Investigate the feasibility of multi-biometrics based on a single sensor
Specifically, combine multi-sample and multi-modal elements to create a system based on face and iris biometrics
Compare performance of multi-biometric approach to single biometric approach
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Sensors – Iris on the Move (IOM)
Developed by Sarnoff Corp. [1]
Designed for Iris recognition
Stand-off and on-the-move
Array of 3 frontal video cameras
– Each frame is 2048 x 2048 px
– Average iris diameter is ~120 px
Synchronized NIR illumination
Image from K. W. Bowyer, K. Hollingsworth, and P. J. Flynn. Image understanding for iris biometrics: A survey. In Computer Vision and Image Understanding, volume 110, pages 281-307. 2008.
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IOM Frame Example
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Sensors – LG IrisAccess 4000 (LG-4000)
Developed by LG Iris [2]
High-quality iris sensor
Short-range, stationary subjects
Average iris diameter is ~250 px
Image from LG Iris Products and Solutions, 2010. URL http://www.lgiris.com/ps/products/irisaccess4000.htm
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LG-4000 Image Example
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Diagram of Approach
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Preprocessing
Stitch and perform histogram matching between corresponding frames
Use template matching to determine translation required to align frames
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Face Detection
Performed on stitched frames
OpenCV version Viola-Jones face detector used [3],[4]
– Trained on whole faces
Faces are cropped according to face detector's estimation of size and location
12
Eye Detection
Used for iris biometrics and for alignment during face matching
Performed in two phases
– Phase 1: Detect eyes in upper quadrants of previously detected faces
– Phase 2: Detect eyes in frames where no faces were found
Both phases use template matching approach to search for specular highlights
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Face and Iris Matcher
Face Matcher
– Colorado State University's implementation of eigenface [5],[6]
– Mahalanobis Cosine: -1 to 1, -1 is perfect match
Iris Matcher
– Modified version of Daugman's algorithm [7]
– Normalized Hamming Distance: 0 to 1.0, 0 is perfect match
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Fusion Summary
Multi-modal and multi-sample scenario
Test and compare multiple fusion approaches
– Score-level
– Rank-level
Three approaches:
– Min rule
– Borda count
– Sum rule
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Min Fusion
Multi-sample, uni-modal, score-level fusion
MinIris = Min{ Ii,j | i=1...n, j=1...G }
MinFace = Min { Fi,j | i=1...m, j=1...G }
Ii,j = HD between i-th probe iris and j-th gallery iris
Fi,j = Mahalanobis distance between i-th probe face and j-th gallery face
n,m = number of irises and faces detected
G = number of gallery subjects
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Borda Fusion
Multi-sample, multi-modal or uni-modal, rank-level fusion
For each probe biometric sample
– Sort gallery subjects by match score (best to worst)
– Cast votes for the top v-ranked gallery subjects
• BordaLinear: VoteWeightn = v + 2 – n
• BordaExp: VoteWeightn = 2v-n
Gallery subject with the most votes is the best match for that probe video
Three variations: BordaIris, BordaFace, and BordaBoth
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Sum Fusion
Multi-sample, multi-modal or uni-modal, score-level fusion
Ii,k = HD between i-th probe iris and k-th gallery iris
FNormi,k = Normalized Mahalanobis distance between i-th probe face and k-th gallery face
n,m = number of irises and faces detected
α,β = weights assigned to face and iris modalities
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Dataset
Collected 1,886 IOM video sets, spanning 363 subjects
– Ranged from 1 to 15 probe videos per subject
Iris gallery consisted of one left eye and one right eye for each subject
– Acquired with an LG-4000
Face gallery consisted of one full face image for each subject
– Manually selected and annotated from stitched IOM frames
– Earliest IOM video with full face available was used to generate gallery image
– Videos used to generate gallery images were not included in probe set
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Detection Results
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Face Matching Results
Mean match score:
-0.281 (σ = 0.213)
Mean non-match score:
0.000 (σ = 0.676)
Independent rank-one:
51.6% (5073/9833)
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Iris Matching Results
Mean match score:
0.398 (σ = 0.053)
Mean non-match score:
0.449 (σ = 0.013)
Independent rank-one:
46.6% (13556/29112)
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Rank-One Recognition Rates
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Comparison Summary
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Conclusions
Investigated fusion of face and iris biometrics from a single sensor
Conducted multi-modal experiments on a genuine dataset of 1886 videos of 363 subjects
Combined multi-modal and multi-sample biometrics, as well as score-level and rank-level fusion
Implemented the proposed multi-biometric workflow on a stand-off and on-the-move sensor
Thus far, the best tested multi-modal approach yielded an increase of 5.4% in rank-one recognition over uni-modal approach
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Acknowledgments & Questions
Datasets used in this work were acquired under funding from the National Science Foundation under grant CNS01-30839, by the Central Intelligence Agency, and by the Technical Support Working Group under US Army Contract W91CRB-08-C-0093.
Current funding is provided by a grant from the Intelligence Advanced Research Projects Activity.
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[1] J. Matey, O. Naroditsky, K. Hanna, R. Kolczynski, D. LoIacono, S. Mangru, M. Tinker, T. Zappia, and W. Zhao. Iris on the Move: Acquisition of Images for Iris Recognition in Less Constrained Environments. In Proceedings of the IEEE, volume 94, pages 1936-1947. November 2006.
[2] LG Iris. LG Iris Products and Solutions, 2010. URL http://www.lgiris.com/ps/products/irisaccess4000.htm.
[3] G. Bradski and A. Kaehler. Learning OpenCV. O'Reilly Media, Inc., 2008.
[4] P. Viola and M. Jones. Rapid Object Detection Using a Boosted Cascade of Simple Features. In 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), volume 1, pages 511-518, 2001.
[5] Colorado State University. Evaluation of Face and Recognition Algorithms, 2010. URL http://www.cs.colostate.edu/evalfacerec/algorithms6.html.
[6] M. Turk and A. Pentland. Face Recognition Using Eigenfaces. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 1991), volume 1, pages 586-591, June 1991.
[7] J. Daugman. How Iris Recognition Works. In 2002 International Conference on Image Processing, volume 1, pages 33-36, 2002.