Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Fingerprint Identification: Classification vs. Indexing
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Handbook of Face Recognition
An Experimental Study on Automatic Face Gender Classification
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Sparse models for gender classification
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Multimodal facial gender and ethnicity identification
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Singular points analysis in fingerprints based on topological structure and orientation field
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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We propose, in this paper, a new biometric identification approach which aims to improve recognition performances in identification systems. We aim to split the identity database into well separated partitions in order to simplify the identification task. In this paper we develop a face identification system and we use the reference algorithms of Eigenfaces and Fisherfaces in order to extract different features describing each identity. These features, which describe faces, are generally optimized to establish the required identity in a classical identification process. In this work, we develop a novel criterion to extract features used to partition the identity database. We develop database partitioning with clustering methods which split the gallery by bringing together identities which have similar features and separating dissimilar features in different bins. Pruning the most dissimilar bins from the query identity features allows us to improve the identification performances. We report results from the XM2VTS database.