Inverse Constrained Maximum Variance Mapping for Face Recognition

Qi Rong Zhang; Jia Nan Gu; Ming Fu Zhang

doi:10.4028/www.scientific.net/AMM.462-463.452

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 462-463Inverse Constrained Maximum Variance Mapping for...

Inverse Constrained Maximum Variance Mapping for Face Recognition

Abstract:

Li et al. [Pattern Recognition 41 (2008) 3287 -- 329 proposed the constrained maximum variance mapping method. The CMVM is globally maximizing the distances between different manifolds. We find out that globally minimizing the distances between the same manifolds can have better recognition than CMVM method on the Yale face database, ORL face database and UMIST face database. Hence we propose to use an inverse constrained maximum variance mapping method (ICMVM) which can be seen as the inverse Laplacian Fisher discriminate criteria. Experiment results suggest that this new approach performs well.

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Periodical:

Applied Mechanics and Materials (Volumes 462-463)

Pages:

452-457

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.462-463.452

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Online since:

November 2013

Authors:

Qi Rong Zhang*, Jia Nan Gu, Ming Fu Zhang

Keywords:

Constrained Maximum Variance Mapping, Face Recognition, Inverse Constrained Maximum Variance Mapping, Inverse Fisher Discriminate Criteria

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