Biometrics: Introduction and Examples. Raymond Veldhuis

Transcription

1 Biometrics: Introduction and Examples Raymond Veldhuis 1

2 Overview Biometric recognition Face recognition Challenges Transparent face recognition Large-scale identification Watch list Anonymous biometrics 3D face recognition 2

3 Biometric recognition Recognition of a person based on physiological characteristics or behavioural traits. Identifying a person Verifying a claimed identity Expected pros: Unique personal characteristic. Assures physical presence. No tokens to loose nor PINS to forget. 3

4 Examples 4

5 Market now 5

6 Market future 6

7 Biometric verification identity claim template database biometric data sensor preprocessing feature extraction classifier noise noise accept/reject 7

8 Optimal classifier Performance measure: False-acceptance rate (FAR, α) False-rejection rate (FRR, β) no feature space yes Likelihood ratio: p( x C) L( x) = > p( x) Optimal decision: False-acceptance rate minimal at fixed false-rejection rate. False-rejection rate minimal at fixed false-acceptance rate. t 8

9 Receiver Operating Characteristic (ROC) High security FRR t EER FAR Easy access 9

10 Log-likelihood ratio (Gaussian case, 1) log px ( C) px ( ) Σ Σ T ( ) 1 T x μ ( x μ ) ( x μ ) 1 ( x μ ) T T T C C C μt μ Σ Σ C T C Total mean Class mean Total covariance matrix Class covariance matrix feature space 10

11 Log-likelihood ratio (Gaussian case, 2) But parameters must be estimated from examples: log px C px ( ) ( ) ˆ T ˆ ˆ T ˆ ( ) ˆ 1 ( ) ( ) ˆ 1 x μ ( ) T T x μt x μc W x μc Σ Σ Small-sample-size problem: Dimensionality of feature vector is (much) greater than number of examples. Estimated covariance matrices are singular. Even if not: Inverses are inaccurate Computationally complex 11

12 Log-likelihood ratio (Gaussian case, 3) log px C px ( ) ( ) ˆ T ˆ ˆ T ˆ ( ) ˆ 1 ( ) ( ) ˆ 1 x μ ( ) T T x μt x μc C x μc Σ Σ Dimensionality reduction by a linear transform: y = Mx log px ( C) px ( ) y ν y ν Λ y ν T T ( ) ( ) 2 1 C C C 12

13 Training, Enrolment and Verification Training identity biometric sensor data preprocessing feature extraction training Enrolment identity biometric sensor data preprocessing feature extraction Verification identity claim biometric sensor data preprocessing feature extraction template database classifier 13 a/r

14 Face verification identity claim template database biometric data sensor preprocessing feature extraction classifier noise noise yes/no Face detection Landmark detection Segmentation Registration Normalization Restoration Geometric Gray value Both 14

15 Performance (FRVT 2002) 15

16 Performance UK Passport Service Biometrics enrolment trail Well conditioned Face verification rate 69% (FRR = 31%) 16

17 Face verification identity claim template database biometric data sensor preprocessing feature extraction classifier noise noise yes/no Face detection Landmark detection Segmentation Registration Normalization Restoration Geometric Gray value Both 17

18 Pre-processing 18

19 Face and landmark detection ADABOOST Cascade of very simple classifiers Trained on feature and non-feature examples Training slow (irrelevant) Detection fast (relevant) 19

20 ADABOOST detection method c 1 c 2 c n D/R (x,y 1 ) >t 1 α 1 >T D/R (x,y n ) >t n α n 20

21 Registration and segmentation original landmarked registered segmented 21

22 Face verification identity claim template database biometric data sensor preprocessing feature extraction classifier noise noise yes/no Face detection Landmark detection Segmentation Registration Normalization Restoration Geometric Gray value Both 22

23 Geometrical features 23

24 Gray value features = + x1 x2 x3 4 x x 5 Feature vector x 24

25 Sensitivity to pose 25

26 Sensitivity to resolution 26

27 Performance example Error rates FAR FRR FAR [%] FRR [%] FRR SotA [%] matching score threshold 27

28 Face-recognition challenges Performance Robustness pose illumination Transparent face recognition Large-scale identification Watch list Anonymous biometrics 3D face recognition 28

29 Transparent face recognition No explicit user action required Home applications Uncontrolled conditions Surveillance applications (Ongoing) authentication on mobile devices 29

30 Large-scale identification Closed set Convenience, home 10 users Open set Security applications, access control Large databases n = 10E+04 to 10E+07 entries. 30

31 Large-scale identification Large databases n = 10E+04 to 10E+07 entries. Usually cooperative subjects Requires extremely good biometric. β total = β c n αtotal = 1 (1 αc) nαc ß High security Easy access α 31

32 Watch lists Surveillance applications Possibly uncontrolled conditions, transparency High detection rate required. Usually medium sized databases n = Non-cooperative subjects. Requires extremely good biometric High security 0.7 p d = 1 β n α = 1 (1 α ) nα total c c c ß Easy access α 32

33 Anonymous Face Recognition enrolment recognition and derolment recognition 33

34 Anonymous Face Recognition Surveillance applications Tracking Detection of suspicious conduct Uncontrolled conditions Many users and identification points complicate problem. Use of other (biometric) characteristics to improve performance 34

35 3D face recognition Recognition based on surface in 3D space Calibrated image Easy registration Robust to lighting variations Difficult acquisition Limited scan volume Spikes and holes 35

36 3D acquisition Laser scanner Stereo vision Structured light 36

37 Detect nose 3D registration Bring image into standard position by rigid 3D transform 37

38 Range image result Result: 3000 numbers representing face geometry 38

39 3D recognition results Testing each test vector against every class average: Training samples per subject EER % % % % % % n=2 (2 training samples per subject) One-to-one testing: similar results 39

40 Conclusion Biometric recognition Face recognition Challenges Performance Robustness Transparent face recognition Large-scale identification Watch list Anonymous biometrics 3D face recognition 40