Heteroscedastic Multilinear Discriminant Analysis for Face Recognition

Authors:
Mehran Safayani;Mohammad Taghi Manzuri Shalmani
Affiliations:
-;-
Venue:
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Year:
2010

Citing 0
Cited 2

Three-dimensional modular discriminant analysis (3DMDA): A new feature extraction approach for face recognition

Computers and Electrical Engineering
Novel Fisher discriminant classifiers

Pattern Recognition

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Abstract

There is a growing attention in subspace learning using tensor-based approaches in high dimensional spaces. In this paper we first indicate that these methods suffer from the Heteroscedastic problem and then propose a new approach called Heteroscedastic Multilinear Discriminant Analysis (HMDA). Our method can solve this problem by utilizing the pairwise chernoff distance between every pair of clusters with the same index in different classes. We also show that our method is a general form of Multilinear Discriminant Analysis (MDA) approach. Experimental results on CMU-PIE, AR and AT&T face databases demonstrate that the proposed method always perform better than MDA in term of classification accuracy.