spatial frequency domain methods for face and iris …kumar/bhagavatula_may2006_talk.pdfiris...

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Vijayakumar Bhagavatula

Vijayakumar BhagavatulaDept. of Electrical and Computer Engineering

Carnegie Mellon UniversityPittsburgh, PA 15213

e-mail: [email protected].: (412) 268-3026

Spatial Frequency Domain Methods for Face and Iris Recognition

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Acknowledgments

Funding SupportTechnology Support Working Group (TSWG), US Government.CyLab, Carnegie Mellon University

Students & ColleagueDr. Marios SavvidesChunyan XieJason Thornton

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Outline

Spatial frequency domain approach (i.e., correlation filters) for biometric recognitionBiometric verification examples

FaceIris

Face Recognition Grand Challenge (FRGC) & Iris Challenge Evaluation (ICE) resultsSummary

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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 variabilityCan we use spatial frequency domain methods for biometric verification?

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Pattern VariabilityFacial appearance may change due to illuminationFingerprint image may change due to plastic deformationIris pattern may change due to rotations, pupil dilation, etc.

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Pattern Recognition Approaches

Statistical methods (e.g., Bayes decision theory)Model-based approachesArtificial neural networksFrequency domain methods (Correlation filters)

No need for segmentation of test imagesGraceful degradationClosed-form expressions

Segment &

Center

ExtractFeatures ClassifyInput Class

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Correlation Filters

MatchNo Match

DecisionTest Image IFFT Analyze

Correlation output

FFT

Correlation Filter

Filter Design . . .Training Images

TrainingRecognition

Ref.: B.V.K. Vijaya Kumar, A. Mahalanobis & Richard D. Juday, Correlation Pattern Recognition, Cambridge University Press, November 2005.

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Peak to Sidelobe Ratio (PSR)

σmeanPeakPSR −

=

1. Locate peak1. Locate peak

2. Mask a small 2. Mask a small pixel regionpixel region

3. Compute the mean and 3. Compute the mean and in a in a bigger region centered at the peakbigger region centered at the peak

PSR invariant to constant illumination changes

Match declared when PSR is large, i.e., peak must not only be large, but sidelobes must be small.

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CMU Pose, Illumination and Expression (PIE) Database One face under 21 illuminations65 subjects

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Train on 3, 7, 16, Train on 3, 7, 16, --> Test on 10.> Test on 10.

Match Quality = 40.95

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Using the same filter as before, Match Quality = 30.60

Occlusion of Eyes

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Match Quality = 22.38

Uncentered Images

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ImpostorUsing someone else’s filterPSR = 4.77

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Features of Correlation Filters

Shift-invariant; no need for centering the test imageGraceful degradationCan handle multiple appearances of the reference image in the test imageClosed-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. 4773-4801, 1992.

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Training ImagesThree face images used to synthesize a correlation filter and an individual eigenspace to perform verificationThe three selected training images consisted of 3 extreme cases (dark left half face, normal face illumination, dark righthalf face).

n = 3 n = 7 n = 16

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0 10 20 30 40 50 60

10

20

30

40

50

60

Equal Error Rate using Individual Eigenface Subspace Method on PIE Database with No Background Illumination

Person

Equ

al E

rror R

ate

Average Equal Error Average Equal Error Rate = 30.8 %Rate = 30.8 %

Equal Error Rate for Individual Eigenspace Method

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Reject Reject

AuthenticateAuthenticateThresholdThreshold

EER using Correlation Filter

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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.

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Face Identification -Train with Illumination Variations

Training Images (selected for each person)

No. misclassifications % Accuracy% Accuracy

3, 7,16 0 100%1,10,16 0 100%2, 7, 16 0 100%4, 7, 13 0 100%1, 2, 7, 16 0 100%3,10,16 0 100%3, 16 0 100%

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Face Identification –Train on Frontal Illumination

Training Images (selected for each person)

No. misclassifications % Accuracy% Accuracy

5,6,7,8,9,10,11,18,19,20 0 100%5, 6, 7, 8, 9, 10, 11, 12 0 100%5, 6, 7, 8, 9, 10 0 100%5, 7, 9,10 0 100%7,10,19 0 100%6,7,8 0 100%8,9,10 0 100%18,19,20 0 100%

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Partial Face Identification

MACE MACE FilterFilter

MACE MACE FilterFilter

Train on 3, 7, 16 for Train on 3, 7, 16 for each personeach person

Train on 3, 7, 16 for Train on 3, 7, 16 for each personeach person

Accuracy = 100 % Accuracy = 100 %

Accuracy = 99.5 % (7 misses)Accuracy = 99.5 % (7 misses)

5 Pixels5 Pixels

30 Pixels30 Pixels

5 Pixels5 Pixels

30 Pixels30 Pixels

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Partial Face Identification

Recognition Accuracy = 99.7 %

Num of mis-classifications = 4

Recognition Accuracy = 95.8 %

Num of mis-classifications = 57

15 Pixels15 Pixels

50 Pixels50 Pixels

50 Pixels50 Pixels

15 Pixels15 Pixels

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 2004.

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Recognition using selected face regions

Using Training set #1 (3 Using Training set #1 (3 extreme lighting images)extreme lighting images)

Using Training set #2 (3 Using Training set #2 (3 frontal lighting images)frontal lighting images)

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Iris BiometricPattern source: muscle ligaments (sphincter, dilator), and connective tissue

Inner boundary (pupil)Outer boundary

(sclera)

Sphincter ring

Dilator muscles

Advantages

Extremely unique pattern.

Remains stable over an individual’s lifetime.

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First Step: Iris Segmentation

Detect iris boundaries

“Unwrap” into polar coordinates

Normalize radius

Standard segmentation procedure: 1

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. 1148-61, Nov. 1993.

Iris image

0 2π0

1

ρ

θ

radius

angle

Example iris mapping

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.

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Gabor Wavelet Iris Encoding (GWIE)

2D and 3D views of Gabor wavelet (real part)

Project onto a family of Gabor wavelet filters

Quantize phase response of each filter to 2 bits

Append bits together

Segmented iris pattern

The resulting bit vector represents the encoded iris features used for matching.

220

2200 /)(/)()(),( βθθαθθωθ −−−−−−= eeerG rri

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Iris Recognition: Correlation FiltersWe 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.

match

no match

FFT-1x

Correlation filter

FFT

Segmented iris pattern

Determining an iris match with a correlation filter

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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.

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Iris Verification with GWIE

Training on first image only:

Overall Equal Error Rate (EER): 4.09 %

Normalized histograms of Hamming similarities (red = imposters, blue = authentics)

Using Masek’s implementation of Daugman’s iris code algorithm

Impostors

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. 2003.

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Iris verification with Correlation FiltersTraining 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)

Normalized match score histograms (red = imposters, blue = authentics)

Impostors

Authentics

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FRGC Dataset: Experiment 4

Generic Training Set consisting of 222 people with a total of 12,776 images

Gallery Set of 466 people (16,028) images total

Feature extraction Feature space generation

Reduced Dimensionality Feature Representation of Gallery Set

16,028

Probe Set of 466 people (8,014) images total

Reduced Dimensionality Feature Representation of Probe Set

8,014

Similarity Matching

Reduced Dimensional Feature Space

project project

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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)

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FRGC Expt. 4 PerformanceEigenfaces (Baseline) results provided by FRGC teamPerformance measured at 0.1 % FAR (False Acceptance Rate)

0

0 . 2

0 . 4

0 . 6

0 . 8

Ex p 4

P CAGS LDA

CFAKCFA- v 1KCFA- v 3

KCFA- v 5

72% @ 0.1FAR

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Source: Jonathon P. Phillips, NIST

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Source: Jonathon P. Phillips, NIST

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SummaryFrequency-domain verification algorithms offer advantages

Shift-invarianceGraceful degradationClosed-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.

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Our Current Research Directions in BiometricsFace Recognition Vendor Test (FRVT) 2006Iris Challenge Evaluation (ICE) 2006Improved face recognitionLarge-population face recognitionFace recognition from low-quality imagesImproved iris recognition Reduced-complexity (e.g., PDA, cell phone, etc.) biometric recognition algorithmsMulti-biometric fusion

We welcome enquiries from funding agencies and contractors interested in supporting us further our technology and/or commercializing it

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