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IEEE CVPR Biometrics 2009

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Dakshina Ranjan Kisku

Face recognition by fusion of local and global matching scores using DS theory: An evaluation with uni-classifier and multi-classifier paradigm

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Face Recognition by Fusion of Local and Global Matching Scores using DS Theory: An Evaluation with Uni-classifier and Multi-classifier Paradigm Authors: D. R. Kisku, M. Tistarelli, J. K. Sing and P. Gupta Presented by: Dr. Linda Brodo (Uniss, Italy) IEEE Computer Society Workshop on Biometrics In Association with CVPR 2009
Agenda of Discussion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Local and global feature-based face recognition ,[object Object],[object Object],[object Object],[object Object]
Challenges of face recognition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
SIFT: feature extraction ,[object Object],[object Object],[object Object],[object Object]
SIFT: steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Invariant SIFT feature extractions are shown on a pair of face images.
Why dynamic and static salient facial parts are considered for face recognition ? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Why dynamic and static salient facial parts are considered for face recognition ? ,[object Object],[object Object],[object Object],[object Object],[object Object]
Local and global matching strategy: Local matching ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Example of matching static facial features.
Local matching ,[object Object],[object Object],[object Object],[object Object],[object Object],Example of independent matching of static and dynamic facial features.
Local matching ,[object Object],[object Object],[object Object]
Local and global matching strategy: Global matching ,[object Object],[object Object],[object Object],[object Object]
Global matching ,[object Object]
Fusion of local and global face matching ,[object Object],[object Object],[object Object],[object Object],[object Object]
Fusion of local and global face matching FD Local and FD Global are the two sets of matching scores. Let  Local  Global are the corresponding power sets of FD Local and FD Global.  is the set of sets in  Local  Global , with C   . Fusion of local and global face matching based on Dempster-Shafer theory
Fusion of local and global face matching The denominator is a normalizing factor which reveals how much the probability assignments on local and Global feature matching are conflicting.
Fusion of local and global face matching
Experimental evaluation and results ,[object Object],[object Object],[object Object]
Evaluation on IITK database ,[object Object],[object Object],[object Object],[object Object],ROC curves determined on IITK face database is shown for both the local and global matching strategy. 93.21 6.79 3.61 9.87 Global matching 95.76 4.24 2.19 6.29 Local matching Recognition rate (%) EER (%) FAR (%) FRR (%) Matching Strategy
Evaluation on ORL database ,[object Object],[object Object],[object Object],[object Object],[object Object],ROC curves determined on ORL face database are shown for local and global matching strategies. 95.83 4.17 2.48 5.86 Global matching 97.39 2.61 1.45 3.77 Local matching Recognition rate (%) EER (%) FAR (%) FRR (%) Matching Strategy
Fusion of local and global matching scores ,[object Object],[object Object]
Fusion of local and global matching scores ROC curves determined from three face databases: IITK, ORL and Yale face databases.
Conclusion ,[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Contd….References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
THANK you !
for contacts: [email_address] [email_address]

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IEEE CVPR Biometrics 2009

  • 1. Face Recognition by Fusion of Local and Global Matching Scores using DS Theory: An Evaluation with Uni-classifier and Multi-classifier Paradigm Authors: D. R. Kisku, M. Tistarelli, J. K. Sing and P. Gupta Presented by: Dr. Linda Brodo (Uniss, Italy) IEEE Computer Society Workshop on Biometrics In Association with CVPR 2009
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15. Fusion of local and global face matching FD Local and FD Global are the two sets of matching scores. Let  Local  Global are the corresponding power sets of FD Local and FD Global.  is the set of sets in  Local  Global , with C   . Fusion of local and global face matching based on Dempster-Shafer theory
  • 16. Fusion of local and global face matching The denominator is a normalizing factor which reveals how much the probability assignments on local and Global feature matching are conflicting.
  • 17. Fusion of local and global face matching
  • 18.
  • 19.
  • 20.
  • 21.
  • 22. Fusion of local and global matching scores ROC curves determined from three face databases: IITK, ORL and Yale face databases.
  • 23.
  • 24.
  • 25.
  • 27. for contacts: [email_address] [email_address]