Online Fingerprint Template Improvement
IEEE Transactions on Pattern Analysis and Machine Intelligence
Template Adaptation based Fingerprint Verification
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Handbook of Biometrics
Template Co-update in Multimodal Biometric Systems
ICB '07 Proceedings of the international conference on Advances in Biometrics
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Semi-supervised PCA-Based face recognition using self-training
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Improving biometric verification systems by fusing Z-norm and F-norm
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Analysis of unsupervised template update in biometric recognition systems
Pattern Recognition Letters
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The problem of biometric template representativeness has recently attracted much attention with the introduction of several template update methods. Automatic template update methods adapt themselves to the intra-class variations of the input data. However, it is possible to hypothesize that the effect of template updating may not be the same for all the clients due to different characteristics of clients present in the biometric database. The goal of this paper is to investigate this hypothesis by explicitly partitioning clients into different groups of the "Doddington's zoo" as a function of their "intrinsic" characteristics, and studying the effect of state of art template "self update" procedure on these different groups. Experimental evaluation on Equinox database with a case study on face verification system based on EBGM algorithm shows the strong evidence of non-uniform update effects on different clients classes and suggest to modify the update procedures according to the client's characteristics.