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… 19 世纪 20042010 Beginning of soft biometrics The term “soft biome- trics” is introduced New definition of soft biometric

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. 19 世纪 20042010 Beginning of soft biometrics The term “soft biome- trics” is introduced New definition of soft biometric

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. 19 世纪 20042010 Beginning of soft biometrics The term “soft biome- trics” is introduced New definition of soft biometric

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. 19 世纪 20042010 Beginning of soft biometrics The term “soft biome- trics” is introduced New definition of soft biometric

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. 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. 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

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.Softbiometrictraitsforpersonalrecog nition systems.In ProceedingsofICBA,pages1–40.Springer,2004. [5].Ailisto,E.Vildjiounaite,M.Lindholm,S.M.Makela,andJ.Peltola.Softbiomet rics–combiningbodyweightandfatmeasurementswithfingerprintbiometrics. PatternRecog-nitionLetters,27(5):325–334,2006

34. The end ！ Thanks!