Soft Biometrics

1. Soft Biometrics 苏毅婧

2. Outline • Introduction • Application • Case study

3. Outline • Introduction • Motivation • Definition • Characteristics • Application • Case study

4. Why use soft biometrics • Biometric systems • Unimodal biometric system • Noise • Non-universality • Impostor • Error rate… • Multimodal biometric system • Cost • Longer verification time • Use soft biometrics as ancillary information

5. Outline • Introduction • Motivation • Definition • Characteristics • Application • Case study

6. Definition • Biometric characteristic should satisfies: • Universality: each person should have the characteristic. • Distinctiveness: any two persons should be sufficiently different in terms of the characteristic. • Permanence: the characteristic should be sufficiently invariant (with respect to the matching criterion) over a period of time. • Collectability: the characteristic can be measured quantitatively.

7. Definition • Alphonse Bertillon firstly introduced the idea for a personal identification system based on biometric.[1] • Colors of eye, hair, beard and skin; • Shape and size of the head… Beginning of soft biometrics The term “soft biome-trics” is introduced New definition of soft biometric 19世纪 2004 2010

8. Definition • A.K.Jain et al. introduced the term “soft biometric”[2] • Soft biometrics provide some information about the individual, but lack of distinctiveness and permanence to sufficiently differentiate any two individuals. Beginning of soft biometrics The term “soft biome-trics” is introduced New definition of soft biometric 19世纪 2004 2010

9. Definition • A.K.Jain et al. introduced the term “soft biometric”[2] • Not expensive to compute, can be sensed at a dis-tance, donot require the cooperation of the surve-illance subjects and have the aim to narrow down the search from a group of candidate individuals. Beginning of soft biometrics The term “soft biome-trics” is introduced New definition of soft biometric 19世纪 2004 2010

10. Definition • A.Dantcheva et al. gave new definition of soft biometric.[3] • Soft biometric traits are physical, behavioral or adhered human characteristics, classifiable in pre-defined human compliant categories. Beginning of soft biometrics The term “soft biome-trics” is introduced New definition of soft biometric 19世纪 2004 2010

11. Soft biometric traits

12. Outline • Introduction • Motivation • Definition • Characteristics • Application • Case study

13. Characteristics(advantages) • Human compliant • Traits are conform with natural human description labels. • Computational efficient • Sensor and computational requirements are marginal. • Enrolment free • Training of the system is performed off-line and without prior Knowledge of the inspected individuals. • Deducible from classical biometrics • Traits can be partly derived from images captured for primary biometric identifier

14. Characteristics(advantages) • Non intrusive • Data acquisition is user friendly or can be fully imperceptible. • Identifiable from a distance • Data acquisition is achievable at long range. • Not requiring the individual’s cooperation • Consent and contribution from the subject are not needed. • Preserving human privacy • The stored signatures are visually available to everyone and serve in this sense privacy.

15. Characteristics(limitations) • Lack of distinctiveness and permanence • Method to overcome the limitation • Fused soft biometric traits

16. Outline • Introduction • Application • Fusion with classical biometric trait • Pruning the search • Human identification • Case study

17. Fusion with classical biometric trait

18. Fusion with classical biometric trait • n users enrolled in the database • X the primary biometric system feature vector • soft biometric feature vector • Bayes rule:

19. Fusion with classical biometric trait • Fingerprint + gender, ethnicity, height[4] • Improvement of 5% • Fingerprint + weight, some weight measures[5] • Error rate 3.9% => 1.5%

20. Outline • Introduction • Application • Fusion with classical biometric trait • Pruning the search • Human identification • Case study

21. Pruning the search

22. Pruning the search • n users enrolled in the database • X the primary biometric system feature vector • soft biometric feature vector • Target : • Filter W and to find a subset of the dataset Z

23. Outline • Introduction • Application • Fusion with classical biometric trait • Pruning the search • Human identification • Case study

24. Human identification

25. Case Study • Soft-biometrics: Unconstrained Authentication in a Surveillance Environment • Simon Denman, Clinton Fookes, Alina Bialkowski, Sridha Sridharan

26. Case Study

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28. Case Study

30. Case Study

31. Case Study

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33. References • [1] H.T.F. Rhodes. Alphonse Bertillon: Father of scientific detection. Pattern Recognition Letters, 1956. • [2] A.K. Jain, S.C. Dass, and K. Nandakumar. Soft biometric traits for personal recognition systems. In Proceedings of ICBA, pages 1–40. Springer, 2004. • [3] A. Dantcheva, C. Velardo, A. DAngelo, and J.-L. Dugelay. Bag ofsoft biometrics for person identification: New trends and challenges. Multimedia Tools and Applications, 51(2):739–777, 2011. 2 • [4] .K.Jain,S.C.Dass,andK.Nandakumar.Softbiometrictraitsforpersonalrecognition systems.In ProceedingsofICBA,pages1–40.Springer,2004. • [5] .Ailisto,E.Vildjiounaite,M.Lindholm,S.M.Makela,andJ.Peltola.Softbiometrics–combiningbodyweightandfatmeasurementswithfingerprintbiometrics. PatternRecog-nitionLetters,27(5):325–334,2006

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