Spatial Frequency Domain Methods for Face and Iris Recognition

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1 Spatial Frequency Domain Methods for Face and Iris Recognition Dept. of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh, PA Tel.: (412)

2 Acknowledgments Funding Support Technology Support Working Group (TSWG), US Government. CyLab, Carnegie Mellon University Students & Colleague Dr. Marios Savvides Chunyan Xie Jason Thornton 2

3 Outline Spatial frequency domain approach (i.e., correlation filters) for biometric recognition Biometric verification examples Face Iris Face Recognition Grand Challenge (FRGC) & Iris Challenge Evaluation (ICE) results Summary 3

Motivation for Spatial Frequency Domain Many biometric modalities

verification methods work in image domain, with their success

frequency-domain methods let the data speak for itself.

automatic target recognition (ATR) applications where the targets

4 Motivation for Spatial Frequency Domain Many biometric modalities produce images Face Fingerprint Iris Palmprint Most biometric verification methods work in image domain, with their success critically depending on the features chosen whereas frequency-domain methods let the data speak for itself. Spatial frequency domain pattern recognition proved successful in automatic target recognition (ATR) applications where the targets exhibit significant variability Can we use spatial frequency domain methods for biometric verification? 4

5 Pattern Variability Facial appearance may change due to illumination Fingerprint image may change due to plastic deformation Iris pattern may change due to rotations, pupil dilation, etc. 5

6 Pattern Recognition Approaches Input Segment & Center Extract Features Classify Class Statistical methods (e.g., Bayes decision theory) Model-based approaches Artificial neural networks Frequency domain methods (Correlation filters) No need for segmentation of test images Graceful degradation Closed-form expressions 6

7 Correlation Filters Test Image FFT IFFT Analyze Decision Training Correlation Filter Correlation output Recognition Training Images... Filter Design Match No Match Ref.: B.V.K. Vijaya Kumar, A. Mahalanobis & Richard D. Juday, Correlation Pattern Recognition, Cambridge University Press, November

Compute the mean and in a bigger region centered at the peak Match declared

8 Peak to Sidelobe Ratio (PSR) PSR invariant to constant illumination changes 1. Locate peak 2. Mask a small pixel region PSR = Peak mean σ 3. Compute the mean and in a bigger region centered at the peak Match declared when PSR is large, i.e., peak must not only be large, but sidelobes must be small. 8

9 CMU Pose, Illumination and Expression (PIE) Database One face under 21 illuminations 9 65 subjects

10 Train on 3, 7, 16, -> > Test on 10. Match Quality =

11 Occlusion of Eyes Using the same filter as before, Match Quality =

12 Uncentered Images Match Quality =

13 Impostor Using someone else s filter PSR =

14 Features of Correlation Filters Shift-invariant; no need for centering the test image Graceful degradation Can handle multiple appearances of the reference image in the test image Closed-form solutions based on well-defined metrics Ref: B.V.K. Vijaya Kumar, Tutorial survey of composite filter designs for optical correlators, Appl. Opt., Vol. 31, pp ,

selected training images consisted of 3 extreme cases (dark left half

15 Training Images Three face images used to synthesize a correlation filter and an individual eigenspace to perform verification The three selected training images consisted of 3 extreme cases (dark left half face, normal face illumination, dark right half face). n = 3 n = 7 n = 16 15

16 Equal Error Rate for Individual Eigenspace Method Equal Error Rate using Individual Eigenface Subspace Method on PIE Database with No Background Illumination Equal Error Rate Average Equal Error Rate = 30.8 % Person 16

17 EER using Correlation Filter Threshold Authenticate Reject 17

18 Face Identification Face recognition: given an input face image, to whom does it belong in a database? If database contains N people where each person has 1 filter, then perform N correlations of the test image, one with each of the filters in the database. The input is assigned to the filter class yielding the largest PSR. 18

19 Face Identification -Train with Illumination Variations Training Images (selected for each person) No. misclassifications % Accuracy 3, 7, % 1,10, % 2, 7, % 4, 7, % 1, 2, 7, % 3,10, % 3, % 19

20 Face Identification Train on Frontal Illumination Training Images (selected for each person) No. misclassifications % Accuracy 5,6,7,8,9,10,11,18,19, % 5, 6, 7, 8, 9, 10, 11, % 5, 6, 7, 8, 9, % 5, 7, 9, % 7,10, % 6,7, % 8,9, % 18,19, % 20

21 Partial Face Identification 5 Pixels Accuracy = 100 % MACE Filter Train on 3, 7, 16 for each person 30 Pixels 5 Pixels 30 Pixels Accuracy = 99.5 % (7 misses) MACE Filter Train on 3, 7, 16 for each person 21

Partial Face Identification 15 Pixels Recognition Accuracy = 99.7 % 50 Pixels Num of mis-classifications = 4 15 Pixels Recognition Accuracy = 95.8 % 50 Pixels Num of mis-classifications = 57 Ref: M.

22 Partial Face Identification 15 Pixels Recognition Accuracy = 99.7 % 50 Pixels Num of mis-classifications = 4 15 Pixels Recognition Accuracy = 95.8 % 50 Pixels Num of mis-classifications = 57 Ref: M. Savvides, B.V.K. Vijaya Kumar and P.K. Khosla, Partial Face Identification using Advanced Correlation Filter Methods, Biometric Technology for Human Identification, SPIE Proc., Vol. 5404, April

23 Recognition using selected face regions Using Training set #1 (3 extreme lighting images) Using Training set #2 (3 frontal lighting images) 23

24 Iris Biometric Pattern source: muscle ligaments (sphincter, dilator), and connective tissue Outer boundary (sclera) Inner boundary (pupil) Dilator muscles Sphincter ring Advantages Extremely unique pattern. Remains stable over an individual s lifetime. 24

First Step: Iris Segmentation Standard segmentation procedure: 1 Iris image Detect iris boundaries Unwrap into polar coordinates Normalize radius radius ρ 1 0 Example iris mapping θ angle 0 2π Iris

25 First Step: Iris Segmentation Standard segmentation procedure: 1 Iris image Detect iris boundaries Unwrap into polar coordinates Normalize radius radius ρ 1 0 Example iris mapping θ angle 0 2π Iris is mapped into a rectangle in normalized polar coordinate system. This segmentation normalizes for scale change and pupil dilation. All iris patterns map to the same size, which makes recognition easier. 1 J.G. Daugman, High Confidence Visual Recognition of Persons by a Test of Statistical Independence, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp , Nov

Gabor Wavelet Iris Encoding (GWIE) 2D and 3D views of Gabor wavelet (real part) G( r, θ ) 2 2 2 0 ) ( r r0 ) / α ( θ θ 0 ) / iω ( θ θ = e e e β 2 Segmented iris pattern Project onto a

26 Gabor Wavelet Iris Encoding (GWIE) 2D and 3D views of Gabor wavelet (real part) G( r, θ ) ) ( r r0 ) / α ( θ θ 0 ) / iω ( θ θ = e e e β 2 Segmented iris pattern Project onto a family of Gabor wavelet filters Quantize phase response of each filter to 2 bits Append bits together The resulting bit vector represents the encoded iris features used for matching. 26

We design a filter for each iris class using a set of training images.

27 Iris Recognition: Correlation Filters We use an alternative method for iris recognition, based on correlation filters. We design a filter for each iris class using a set of training images. Determining an iris match with a correlation filter FFT x FFT -1 match Segmented iris pattern Correlation filter no match 27

Experiments on CASIA Database 108 iris classes 7 images per class, collected in 2 sessions resolution 280 by 320 collected with IR illumination We applied correlation

28 Experiments on CASIA Database 108 iris classes 7 images per class, collected in 2 sessions resolution 280 by 320 collected with IR illumination We applied correlation filters to sections of each segmented iris (corresponding to left and right sides of the iris). We set the recognition threshold in order to compute Equal Error Rates. 28

29 Iris Verification with GWIE Using Masek s implementation of Daugman s iris code algorithm Training on first image only: Overall Equal Error Rate (EER): 4.09 % Impostors Authentics Normalized histograms of Hamming similarities (red = imposters, blue = authentics) Libor Masek, Peter Kovesi. MATLAB Source Code for a Biometric Identification System Based on Iris Patterns. The School of Computer Science and Software Engineering, The University of Western Australia

30 Iris verification with Correlation Filters Training on first image only: (using a single iris image for training) Overall Equal Error Rate (EER): 0.94 % Classes w/ complete score separation: 98 (of 108) Impostors Authentics Normalized match score histograms (red = imposters, blue = authentics) 30

31 FRGC Dataset: Experiment 4 Generic Training Set consisting of 222 people with a total of 12,776 images Feature extraction Feature space generation project Reduced Dimensional Feature Space Reduced Dimensionality Feature Representation of Gallery Set 16,028 Reduced Dimensionality Feature Representation of Probe Set 8,014 project Similarity Matching Gallery Set of 466 people (16,028) images total Probe Set of 466 people (8,014) images total 31

32 FRGC Baseline Results The verification rate of PCA is about 12% at False Accept Rate 0.1%. ROC curve from P. Jonathan Phillips et al (CVPR 2005) 32

33 FRGC Expt. 4 Performance Eigenfaces (Baseline) results provided by FRGC team Performance measured at 0.1 % FAR (False Acceptance Rate) 0.1FAR PCA GSLDA CFA KCFA-v1 KCFA-v3 KCFA-v5 0 Exp 4 33

34 Source: Jonathon P. Phillips, NIST 34

35 Source: Jonathon P. Phillips, NIST 35

36 Summary Frequency-domain verification algorithms offer advantages Shift-invariance Graceful degradation Closed-form solutions Correlation filters offer excellent performance for image biometrics (e.g., face, iris, fingerprint and palmprint). Same correlation engine can be used for multiple biometric modalities. 36

37 Our Current Research Directions in Biometrics Face Recognition Vendor Test (FRVT) 2006 Iris Challenge Evaluation (ICE) 2006 Improved face recognition Large-population face recognition Face recognition from low-quality images Improved iris recognition Reduced-complexity (e.g., PDA, cell phone, etc.) biometric recognition algorithms Multi-biometric fusion We welcome enquiries from funding agencies and contractors interested in supporting us further our technology and/or commercializing it 37

Image Biometric Verification in Spatial Frequency Domain

Image Biometric Verification in Spatial Frequency Domain 1 Acknowledgments Dr. Marios Savvides Dr. Chunyan Xie Jason Thornton Krithika Venkataramani Pablo Hennings Technology Support Working Group (TSWG)