1. Performance Testing “ Guide to Biometrics” - chapter 7 “ An Introduction to Evaluating Biometric Systems” by Phillips et al., IEEE Computer, February 2000, pp 56-63 Presented By: Xavier Palathingal September 21 st, 2005

2. Background Two biometric capabilities( matching and ranking) and biometric system errors Chapter 5 – 1:1 Biometric Matching Chapter 6 – 1:m Biometric Searching Relate “error quotes” to error definitions Look at accuracy numbers and reconstruct and interpret them

3. Overview Measuring performance Technology Evaluations Scenario Evaluations Operational Evaluations Comparison of the methods Limits to Evaluations

4. Overview (cont…) Implications of error rates Biometric Authentication - “Why does it reject me?” Biometric Screening – “Why does it point to me?” Face, Finger and Voice Iris and Hand geometry Signature Summary of verification accuracies

5. Overview (cont…) Identification System Testing Biometric data and ROC and CMC Biometric search engines 1:m search engine testing Face Recognition and Verification Test 2000 [ FVRT 2000 ] FVRT 2002 Face, Finger and Voice

6. Measuring Performance Evaluation protocols Why measure performance ? Determines how you test the system, select the data and measure performance Evaluation shouldn’t be too hard or too easy Is just right when it spreads performance over a range that lets to distinguish

7. Measuring Performance 1 -Technology Evaluations On laboratory or prototype algorithms “testing on databases” Move from general to specific “training” data A Sequestered “test” data Q Two phases Training phase Competitive testing phase

8. Phase 1 of Technology Evaluation- Training phase The algorithm is trained using “training” data A = (A 1 U A 2 U …) Then tested on newly made available sequestered “test” data Q

9. Phase 2 of Technology Evaluation - Competitive testing phase Using database D for each matcher Z, a set of match (genuine) scores X={X 1,X 2,…,X N } and a set of non-match scores Y={Y 1,Y 2,….,Y N } are generated. FMR and FNMR [FAR and FRR] are calculated as a function of threshold T

10. Measuring Performance 2 - Scenario Evaluations Tests complete biometric systems under conditions that model real world applications Combination of sensors and algorithms “office environment”, “user tests”

11. Measuring Performance 3 - Operational Evaluations Similar to scenario evaluations Scenario test – class of applications Operational test – specific algorithm for a specific application Performed at the actual site Using actual subjects/areas Usually not very repeatable

12. Comparison of methods Academia tend to use databases i.e.; technology evaluations acquisition procedures user population is closed in scenario evaluations Not “double blind” – technology and scenario

13. Limits to evaluation Biometric authentication should be mandatory to the whole user population User population should be fairly represented Subjects should be unaware of the matching decision Only realistic form of testing is operational evaluation One cannot measure the true FAR or true FNR – nobody except the actual subject knows Attempt to measure these “hidden” system parameters will be by trying to defeat the biometric system

14. Implication of error rates Biometric Authentication “Why does it reject me?” Verification protocol – frequent flyer smartcard with biometric - fingerprint template on a smartcard - unique frequent flyer no. and smartcard - FRR = 3% (typical for finger) - 5000 people per hr [Newark airport] in a 14 hr day.03 x 5000 x 14 = 2100 - will have to handled through exception handling procedures

15. Implication of error rates Biometric Screening “Why does it point to me?” Screening protocol – passenger face images with government face image database - a system that checks a face against a negative database N of n=25 alleged terrorists - FPR = 0.1% - 300 people request access to a flight 25 x 300 = 7500 matches 7500 x.001 = 7 false positives

16. Implication of error rates Biometric Screening “Why does it point to me?” The no. of false positives, FPR(n) ≈ n x FPR(1) Matching a positive data set M of m subjects requires m matches against a database N of n terrorists m = 300 n = 25 # false positives for plane = m x FPR(n) = m x n x FPR(1)

17. Face, Finger and Voice Technology evaluations FARs are operating around 10%

18. Iris “normal office environment”, with 200 volunteers over a period of 3 months In identification mode, not in verification mode High FRR may be due to environmental error, reflection from glasses, user difficulty

19. Hand Geometry Group of 50 users. 200 volunteers over a 3 month period

20. Signature Does not have the characteristic of permanence Accept = genuine, reject = forgery Zero-effort forgery, Home-improved forgery, Over-the-shoulder forgery, Professional forgery

21. Signature (cont….) Improvement of two-try over one-try indicates poor habituation of the biometric on that particular device

22. Summary of verification accuracies Best error rates found in literature One main thing is the volume

23. Identification system testing: Biometric data and ROC,CMC Biometric capabilities like ranking and matching need to be developed by modeling biometric data and training using biometric data Two different biometric statistics – ROC and CMC ROC – measures the capabilities of a match engine s(B’,B) with some fixed t 0 or as a function of some operating threshold T CMC – measures the capabilities of a rank engine R((B 1,B 2 ),B’ l ) with ordered entries (B 1,B 2 ) € M and some unknown sample B’ l

24. Biometric search engines A hybrid approach - ranking followed scoring Input to the 1:m search engine - B’ l, the biometric sample Output - vector C K (B’ l ) =(ID (1),…ID (K) ) T The 1:m search engine with an enrollment database of M is defined as : C K = (B (1),B (2),….,B (K) ) T = (ID (1),ID (2),…,ID (K) ) T

25. Biometric search engines (cont…) A possible architecture: - A biometric rank engine which determine some reordering C m of vector M by repeatedly applying ranking - A biometric match engine determine using a scoring function s(B’ l,B (k) ) and decision threshold t 0 (B’ l ),a short candidate vector C K of the K top candidates

26. 1:m search engine testing The big distinction of a 1:m search engine compared to a 1:1 matcher - prerequisite of an enrollment database M = (B 1,B 2,….B M ) T We select the first m samples as database samples [9] For other samples, denoted as {B’ l,l=m+1}= D\M, a rank ř(B’ l ) is estimated as follows: 1.Computes the sets of scores X l = {s(B’ l,B i ); i = 1,….,m} for l = m + 1

27. 1:m search engine testing (cont..) 2. Sort these scores: X ~ l = (s(B’ l,B (1) ),s(B’ l,B (2) ),….s(B’ l,B (m) )) T such that s(B’ l,B (k) ) > s(B’ l,B (k+1) ), 1 ≤ k < m 3. If (B’ l,B (k) ) is the mated pair, i.e., if B i = B (k) matches B’ l, ř(B’ l ) = k

28. Face Recognition and Verification Test 2000 First attempt to characterize performance measures 5 participating vendors had to compute an all-against-all match of a database of 13,872 face images Some results: 1.Compression does not affect performance adversely 2.Pose changes up to 25 degrees was handled by algorithms, beyond 40 the performance degrades sharply 3.Images taken 12 or more months apart are difficult to recognize 4.Distance between camera and person matters a lot 5.Identification is more sensitive to expression changes than verification is

29. FRVT 2002 An increase in database size Difference in results in plain verification tasks – K =sorted list size, m =gallery size

30. Thank you ! Especially to: Dr.Bebis for suggesting the additional paper Reza and Chang for help with the scanner