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Communication Dans Un Congrès Année : 2005

Normalized Radial Basis Function Networks and Bilinear Discriminant Analysis for Face Recognition

Résumé

In this paper, we present a novel approach for face recognition, using a new subspace method called bilinear discriminant analysis (BDA) and normalized radial basis function networks (NRBFNs). In a first step, BDA extracts the features that enhance separation between classes by using a generalized bilinear projection-based Fisher criterion, computed from image matrices directly. In a second step, the features are fed into a NRBFN that learns class conditional probabilities. This results in an efficient and computationally simple open-world identification process. Experimental results assess the performance and robustness of the proposed algorithm compared to other subspace methods combined with NRBFNs, in the presence of variations in head poses, facial expressions, and partial occlusions.
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Dates et versions

hal-00452462 , version 1 (06-09-2013)

Identifiants

  • HAL Id : hal-00452462 , version 1

Citer

Muriel Visani, Christophe Garcia, Jean-Michel Jolion. Normalized Radial Basis Function Networks and Bilinear Discriminant Analysis for Face Recognition. IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS 2005), Sep 2005, Como, Italy. pp.342-347. ⟨hal-00452462⟩
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