A New Clustering Approach for Face Identification


We propose in this paper a search approach which aim to improve identification in biometric databases. We work with face images and we develop appearance-based Eigenfaces and Fisherfaces methods to generate holistic and discriminant features and attributes. These features, which describe faces, are often used to establish the required identity in a classical identification process. In this work we introduce a clustering process upstream the identification process which divides faces into partitions according to their features similarities. Indeed, we aim to split biometric databases into partitions in order to simplify the recognition task within these databases. This paper describes the proposed clustering approach, the Eigenfaces and Fisherfaces representation methods and preliminary clustering results on the XM2VTS data corpus.

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@article{Chaari2008ANC, title={A New Clustering Approach for Face Identification}, author={Abdessattar Chaari and Sylvie Lelandais and Mohamed Ben Ahmed}, journal={2008 First Workshops on Image Processing Theory, Tools and Applications}, year={2008}, pages={1-6} }