Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Template Adaptation based Fingerprint Verification
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
A Confidence-Based Update Rule for Self-updating Human Face Recognition Systems
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Template Update Methods in Adaptive Biometric Systems: A Critical Review
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Temporal analysis of biometric template update procedures in uncontrolled environment
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
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
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Self-update is the most commonly adopted biometric template update technique in which the system adapts itself to the confidently classified samples. However, the recent works indicate that self-update has limited capability to capture samples representing significant intra-class variations. As an alternative, a biometric template update technique based on the graph-based representation is proposed. This technique can potentially capture samples with significant variations, resulting in efficient adaptation. Until now, the efficacy of these adaptation techniques has been proven only on the basis of experimental evaluations on small data sets. The contribution of this paper lies in (a) conceptual explanation of the functioning of self-update and graph-based techniques to template adaptation leading to efficacy of the latter and (b) evaluation of the performance of these adaptation techniques in comparison to the baseline system without adaptation. Experiments are conducted on the large DIEE data set, explicitly collected for this aim. Reported results validate the superiority of the graph-based technique over self-update.