A new graph-based approach for biometric fusion at hybrid rank-score level


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In 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 level

  1. 1. thesis A new graph-based approach for biometric fusion at hybrid rank-score level relatore Ch.mo Prof. Carlo Sansone correlatore Ing. Emanuela Marasco candidate Sotiris Mitracos 2009/2010
  2. 2. Background <ul><li>A Multibiometric system uses multiple sources to acquire biometric information in order to establish the identity of an individual. </li></ul><ul><li>Problem: reducing Error Rate in identification procedure </li></ul>Contribution <ul><li>An innovative framework for fusion at hybrid rank-score level based on graph models </li></ul>
  3. 3. <ul><li>Biometric Identification: finding the person ’ s identity by biometric information in a previously created database </li></ul><ul><li>Errors: </li></ul><ul><ul><li>FAR (False Acceptance Rate) </li></ul></ul><ul><ul><li>FRR (False Rejection Rate) </li></ul></ul>
  4. 4. Post-Matching Fusion <ul><li>Score-Level </li></ul><ul><li>Product rule </li></ul><ul><li>Sum rule </li></ul><ul><li>Maximum rule </li></ul><ul><li>Minimum rule </li></ul><ul><li>Mean value rule </li></ul><ul><li>Rank-Level </li></ul><ul><li>Highest Rank </li></ul><ul><li>Borda Count </li></ul><ul><li>Logistic Regration </li></ul><ul><li>Decision-Level </li></ul><ul><li>AND/OR Rules </li></ul><ul><li>Majority Voting </li></ul><ul><li>Weighted Majority Voting </li></ul>
  5. 5. Proposed Approach <ul><li>Graph-Identity Representation </li></ul><ul><li>Main idea : adding to the probe identity cohort information </li></ul><ul><li>Model : graph-based modelling </li></ul>Each unimodal matcher generates a Top-k candidate list Each Top-k list is used to generate a 2-level-non-complete graph <ul><li>Probe Graph </li></ul><ul><li>Gallery Graph </li></ul>list of candidates that could be identified as the genuine identity
  6. 6. Identification as graph-matching problem <ul><li>Computing Graph Similarity Score </li></ul><ul><li>G 1 = Probe Graph; G 2 = Gallery Graph </li></ul><ul><li>Let s[R 1 ] be the matching score of the root of the given graph </li></ul><ul><li>s[R j ] be the matching score of the j th neighbour </li></ul><ul><li>d 1 =s[R 1 ]-s[R j ] and d 2 =s[R 1 ]-s[Rj] the matching score difference between root identity and the j th common neighbour of the probe graph (d 1 ) and of the gallery graph (d 2 ) </li></ul><ul><li>Graph Similarity Score sim : </li></ul><ul><li>CN = # of common neighbours, </li></ul><ul><li>k = # of identities of the candidate list </li></ul><ul><li>sim(j) = min⁡ (d 1 /d 2 ,d 2 /d 1 )  j = common neighbour </li></ul><ul><li>sim(j) = 0 otherwise </li></ul>
  7. 7. Graph-based Fusion <ul><li>Graph Score Normalization </li></ul><ul><ul><li>For each matcher we define as the GSS corresponding to its EER. </li></ul></ul><ul><li>Graph Score Fusion </li></ul><ul><li>Let j=1:J be the unimodal matcher. </li></ul><ul><ul><li>Each sim j is normalized according to with and assigned to Top-k j ; </li></ul></ul><ul><ul><li>All Top-k j lists are merged in a Top-k×J list of candidates , where each k of the j th macher candidate has a fusion score s jk equal to where </li></ul></ul><ul><ul><li>Set L the set of distinct candidates and id(jk) the candidate given by Rank- k of matcher j </li></ul></ul><ul><ul><li>and the genuine identity is the identity l associated to the max . </li></ul></ul>K = # of identities of the Top-k list
  8. 8. Fused Graph <ul><li>Computational Complexity </li></ul><ul><li>Let n be the number of identities </li></ul><ul><li>T(n) = J×f(n) (1) + J×O(nlogn) (2) + J×O(k) (3) + J×O(k 2 ) (4) + J×O(1) (5) + O(k×J) (6) + O(k×J) (7) + O(k×J) (8) </li></ul><ul><li>Considering f(n) = O(n), n>>k×J and n>k 2 </li></ul><ul><li>T(n) = J×O(nlogn) = O(nlogn). </li></ul>
  9. 9. Experimental Results <ul><li>Dataset </li></ul><ul><li>WVU </li></ul><ul><li>5 modalities: 1 Face, 4 Fingerprints (L1, L2, R1, R2); </li></ul><ul><li>5 samples per identity (4 probes, 1 gallery); </li></ul><ul><li>240 identities. </li></ul><ul><li>BioSecure </li></ul><ul><li>4 modalities: 1 Face (fnf1), 3 Fingerprints (fo1, fo2, fo3); </li></ul><ul><li>3 samples per identity (2 probes, 1 gallery) </li></ul><ul><li>Training: </li></ul><ul><ul><li>51 identities (Development Set) </li></ul></ul><ul><li>Test: </li></ul><ul><ul><li>156 identities (Evaluation Set) </li></ul></ul><ul><li>Used matchers (for WVU) </li></ul><ul><li>Bozorth3 for Fingerprints </li></ul><ul><li>PCA for Faces </li></ul>
  10. 10. Case Study: ID 41 – P4 <ul><li>Create Top10 lists </li></ul><ul><li>Create probe and gallery graphs </li></ul><ul><li>Evaluate Graph Similarity Scores </li></ul><ul><li>Evaluate </li></ul><ul><li>Normalize Graph Similarity Scores </li></ul><ul><li>Create Top50 list </li></ul><ul><li>Create Fusion Graph and evaluate </li></ul><ul><li>the genuine identity through GBF </li></ul><ul><li>Other schemes behaviour for ID 41 </li></ul>
  11. 11. Evaluation Procedure <ul><li>We have set k=10; thus Top-k = Top10 . Furthermore, for WVU J=5, for BioSecure J=4. </li></ul><ul><li>GSS </li></ul><ul><li>GBF </li></ul>Evaluation on WVU with 10-Fold Cross Validation Evaluation on BioSecure Evaluation Set
  12. 12. Further Analysis <ul><li>Top10 analysis </li></ul><ul><li>k=10 for Top-k guarantees a high percentage </li></ul><ul><li>of finding the correct identity into the list. </li></ul><ul><li>The more the identity is far from the top of the list, the more its p for fusion score evaluation has to be high. </li></ul>
  13. 13. Conclusions <ul><li>This 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 . </li></ul><ul><li>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. </li></ul><ul><li>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.  </li></ul><ul><li>This approach guarantees a high accuracy , despite the weakness of unimodal matchers. </li></ul>Future Works It 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!!