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For more information, contact me at akismitracos [ a t ] gmail . comIn this paper we define a new approach to biometric fusion, characterized by the use of graph structure to represent identities, starting from a hybrid rank-score fusion level.We define the proposed framework, with the description of the mapping identity-list-graph, with the use of the cohort theory, and then we explain the steps to perform the Graph Similarity Score and the Graph Based Fusion and their computational complexity.Subsequently, we apply the proposed method to two different dataset, in order to evaluate its accuracy and to compare the obtained results against the employment of the other fusion schemes previously illustrated, and then we analyze the results themselves, even by means of a Case Study.Finally we make reflections about what has been achieved and which could be the possible future works exploiting our framework.
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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!!
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