soft biometrics 苏毅婧. outline introduction application case study

Soft Biometrics

苏毅婧

Outline

• Introduction• Application• Case study

Outline

• Introduction– Motivation– Definition– Characteristics

• Application• Case study

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

Outline

• Introduction– Motivation– Definition– Characteristics

• Application• Case study

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.

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 2004 2010

Beginning of soft biometrics

The term “soft biome-trics” is introduced

New definition of soft biometric

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 2004 2010

Beginning of soft biometrics

The term “soft biome-trics” is introduced

New definition of soft biometric

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 2004 2010

Beginning of soft biometrics

The term “soft biome-trics” is introduced

New definition of soft biometric

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 2004 2010

Beginning of soft biometrics

The term “soft biome-trics” is introduced

New definition of soft biometric

Soft biometric traits

Outline

• Introduction– Motivation– Definition– Characteristics

• Application• Case study

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

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.

Characteristics(limitations)

• Lack of distinctiveness and permanence

• Method to overcome the limitation– Fused soft biometric traits

Outline

• Introduction• Application– Fusion with classical biometric trait– Pruning the search– Human identification

• Case study

Fusion with classical biometric trait

Fusion with classical biometric trait

• n users enrolled in the database• X the primary biometric system feature vector • soft biometric feature vector• Bayes rule:

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%

Outline

• Introduction• Application– Fusion with classical biometric trait– Pruning the search– Human identification

• Case study

Pruning the search

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

Outline

• Introduction• Application– Fusion with classical biometric trait– Pruning the search– Human identification

• Case study

Human identification

Case Study

• Soft-biometrics: Unconstrained Authentication in a Surveillance Environment– Simon Denman, Clinton Fookes, Alina Bialkowski,

Sridha Sridharan

Case Study

Case Study

Case Study

Case Study

Case Study

Case Study

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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