On-Line Fingerprint Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Handbook of Face Recognition
EURASIP Journal on Advances in Signal Processing
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
A Theoretical and Experimental Analysis of Template Co-update in Biometric Verification Systems
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Modelling FRR of Biometric Verification Systems Using the Template Co-update Algorithm
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Semi-supervised learning with very few labeled training examples
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
When Does Cotraining Work in Real Data?
IEEE Transactions on Knowledge and Data Engineering
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
Self-corrective character recognition system
IEEE Transactions on Information Theory
Analysis of co-training algorithm with very small training sets
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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Performance of mono- and multi-modal biometric systems depends on the representativeness of enrolled templates. Unfortunately, error rate values estimated during the system design are subject to variations due to several aspects: intra-class variations arising on small-medium time-window, and ageing, which is the natural process involving any biometrics. This causes the increase of the False Rejection Rate (genuine users are no more recognized) or the False Acceptance Rate (impostors are misclassified as genuine users), or both. In fact, several vendors strongly suggest to repeat enrolment sessions in order to collect, over time, a set of templates representative enough. As alternative, automatic template update algorithms, which exploit the own-knowledge of the mono- or multi-modal biometric system, on a batch of samples collected during system operations without the human supervision, have been proposed. Preliminary experimental results have shown that these algorithms are promising, but the motivation of their behaviour has not yet been explained. This paper is aimed to fill such gap, by showing that behaviour of self- and co-update may be explained by exploiting the concept of path-based clustering. Therefore, problems as 'intra-class' variations and ageing are dependent on the path-based cluster followed by each algorithm. Moreover, we show that the performance of co-update is superior than that of self-update, by a simulative model. The path-based clustering theory applied to self- and co-update algorithms, as well as the proposed model, are experimentally validated on the large DIEE Multimodal data set, the only one publicly available and explicitly conceived for comparing template update algorithms.