NDLI: A New Clustering Approach for Face Identification

Content Provider	IEEE Xplore Digital Library
Author	Chaari, A. Lelandais, S. Ben Ahmed, M.
Copyright Year	2008
Description	Author affiliation: Nat. Sch. of Comput. Sci., Manouba Univ., La Manouba (Ben Ahmed, M.) \|\| IBISC Lab., Evry Univ., Evry (Chaari, A.; Lelandais, S.)
Abstract	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.
Starting Page	1
Ending Page	6
File Size	268988
Page Count	6
File Format	PDF
ISBN	9781424433216
DOI	10.1109/IPTA.2008.4743736
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-23
Publisher Place	Tunisia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biometrics Eigenfaces Error analysis Scalability Laboratories Magnetic resonance Spatial databases learning Clustering Biometry Image databases Face identification Authentication Fisherfaces Large-scale systems Probes
Content Type	Text
Resource Type	Article

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