A new graph-based approach for biometric fusion at hybrid rank-score levelFacoltà di IngegneriaCorso di Studi in Ingegneria Informatica

thesisthesis

relatore

Ch.mo Prof. Carlo Sansone

correlatore

Ing. Emanuela Marasco

candidate

Sotiris Mitracos

A new graph-based approach for biometric A new graph-based approach for biometric fusion at hybrid rank-score levelfusion at hybrid rank-score level2009/2010

A new graph-based approach for biometric fusion at hybrid rank-score levelFacoltà di IngegneriaCorso di Studi in Ingegneria Informatica

BackgroundBackground

A Multibiometric system A Multibiometric system uses multiple uses multiple sources to acquire biometric information sources to acquire biometric information in order to establish the identity of an in order to establish the identity of an individual.individual.

Problem: Problem: reducing Error Rate in reducing Error Rate in identification procedureidentification procedure

ContributionContribution

• An innovative framework for fusion at hybrid rank-score level based on graph models

A new graph-based approach for biometric fusion at hybrid rank-score levelFacoltà di IngegneriaCorso di Studi in Ingegneria Informatica

• Biometric Identification: Biometric Identification: finding the person’s identity by biometric finding the person’s identity by biometric information in a previously created databaseinformation in a previously created database

• Errors:Errors:• FAR (False Acceptance Rate)• FRR (False Rejection Rate)

A new graph-based approach for biometric fusion at hybrid rank-score levelFacoltà di IngegneriaCorso di Studi in Ingegneria Informatica

Post-Matching FusionPost-Matching Fusion

Score-LevelProduct ruleSum ruleMaximum ruleMinimum ruleMean value rule

Rank-LevelHighest RankBorda CountLogistic Regration

Decision-LevelAND/OR RulesMajority VotingWeighted Majority Voting

A new graph-based approach for biometric fusion at hybrid rank-score levelFacoltà di IngegneriaCorso di Studi in Ingegneria Informatica

Proposed ApproachProposed ApproachGraph-Identity Graph-Identity RepresentationRepresentationMain ideaMain idea: adding to the probe : adding to the probe identity identity cohort informationcohort information

ModelModel: graph-based modelling: graph-based modelling Each unimodal matcher generates a Top-k candidate listEach Top-k list is used to generate a 2-level-non-complete graph

Probe Graph

Gallery Graph

list of candidates that could be identified as the genuine identity

A new graph-based approach for biometric fusion at hybrid rank-score levelFacoltà di IngegneriaCorso di Studi in Ingegneria Informatica

Identification as graph-matching problemIdentification as graph-matching problem

Computing Computing Graph Similarity ScoreGraph Similarity Score

G1 = Probe Graph; G2 = Gallery Graph

Let s[R1] be the matching score of the root of the given graph

s[Rj] be the matching score of the jth neighbour

d1=s[R1]-s[Rj] and d2=s[R1]-s[Rj] the matching score difference between root identity and the jth common neighbour of the probe graph (d1) and of the gallery graph (d2)

Graph Similarity Score sim:

CN = # of common neighbours,

k = # of identities of the candidate list

sim(j) = min (d1/d2,d2/d1) j = common neighbour

sim(j) = 0 otherwise

A new graph-based approach for biometric fusion at hybrid rank-score levelFacoltà di IngegneriaCorso di Studi in Ingegneria Informatica

Graph-based FusionGraph-based Fusion

Graph Score NormalizationGraph Score Normalization For each matcher we define as the For each matcher we define as the GSSGSS corresponding to its EER. corresponding to its EER.

Graph Score FusionGraph Score Fusion Let j=1:J be the unimodal matcher.

– Each simj is normalized according to with and assigned to Top-kj;

– All Top-kj lists are merged in a Top-k×J list of candidates, where each k of the jth macher candidate has a fusion score sjk equal to where

– Set L the set of distinct candidates and id(jk) the candidate given by Rank-k of matcher j

1. and the genuine identity is the identity l associated to the max.

K = # of identities of the Top-k list

A new graph-based approach for biometric fusion at hybrid rank-score levelFacoltà di IngegneriaCorso di Studi in Ingegneria Informatica

Fused GraphFused Graph

Computational ComplexityComputational Complexity

Let Let nn be the number of identities be the number of identitiesT(n) = J×f(n)(1) + J×O(nlogn)(2) + J×O(k)(3) + J×O(k2)(4) + J×O(1)(5) + O(k×J)(6) + O(k×J)(7) + O(k×J)(8)

Considering Considering f(n) = O(n), n>>k×J and n>k2

T(n) = J×O(nlogn) = O(nlogn).

A new graph-based approach for biometric fusion at hybrid rank-score levelFacoltà di IngegneriaCorso di Studi in Ingegneria Informatica

Experimental ResultsExperimental Results

DatasetDatasetWVU

•5 modalities: 1 Face, 4 Fingerprints (L1, L2, R1, R2);•5 samples per identity (4 probes, 1 gallery);•240 identities.

BioSecure•4 modalities: 1 Face (fnf1), 3 Fingerprints (fo1, fo2, fo3);•3 samples per identity (2 probes, 1 gallery)•Training:

51 identities (Development Set)•Test:

156 identities (Evaluation Set)Used matchers (for WVU)Used matchers (for WVU)

•Bozorth3 for Fingerprints•PCA for Faces

A new graph-based approach for biometric fusion at hybrid rank-score levelFacoltà di IngegneriaCorso di Studi in Ingegneria Informatica

1. Create Top10 lists

2. Create probe and gallery graphs3. Evaluate Graph Similarity Scores

4. Evaluate

5. Normalize Graph Similarity Scores

6. Create Top50 list

7. Create Fusion Graph and evaluate the genuine identity through GBF

Other schemes behaviour for ID 41

Case Study: ID 41 – P4Case Study: ID 41 – P4

A new graph-based approach for biometric fusion at hybrid rank-score levelFacoltà di IngegneriaCorso di Studi in Ingegneria Informatica

Evaluation ProcedureEvaluation ProcedureWe have set k=10; thus Top-k=Top10. Furthermore, for WVU J=5, for BioSecure J=4.•GSS

•GBF

Evaluation on WVU with 10-Fold Cross Validation

Evaluation on BioSecure Evaluation Set

A new graph-based approach for biometric fusion at hybrid rank-score levelFacoltà di IngegneriaCorso di Studi in Ingegneria Informatica

Further AnalysisFurther AnalysisTop10 analysisTop10 analysisk=10 for Top-k guarantees a high percentage

of finding the correct identity into the list.

The more the identity is far from the top of the list, the more its p for fusion score evaluation has to be high.

A new graph-based approach for biometric fusion at hybrid rank-score levelFacoltà di IngegneriaCorso di Studi in Ingegneria Informatica

ConclusionsConclusionsThis presentation has shown a new method to perform a multimodal biometric matching by using a new

graph-based approach for the fusion at hybrid rank-score level.

One of the most important advantage of this method is the use of graph approach, which let us have a simple representation of each identity and makes the fusion easier to perform.

Another important feature of our approach is the use of a “competence level” of each unimodal matcher, by means of the introduction of a penalty assigned to each one. 

This approach guarantees a high accuracy, despite the weakness of unimodal matchers.

Future WorksFuture WorksIt would be interesting to learn which behaviour the realized approach will have while scaling the problem, i.e.

using a huge dataset.

Another analysis could be done by introducing quality measures inside the scheme.

Graph-based approach could also be used in unimodal matchers to increase accuracy since the beginning.

Thank You for Your Attention!!Thank You for Your Attention!!