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Heteroscedastic multilinear discriminant analysis for face recognition

Safayani, M ; Sharif University of Technology | 2010

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  1. Type of Document: Article
  2. DOI: 10.1109/ICPR.2010.1042
  3. Publisher: 2010
  4. Abstract:
  5. 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
  6. Keywords:
  7. Chernoff distance ; Classification accuracy ; Face database ; Heteroscedastic ; High dimensional spaces ; New approaches ; Subspace learning ; Discriminant analysis ; Face recognition
  8. Source: Proceedings - International Conference on Pattern Recognition, 23 August 2010 through 26 August 2010, Istanbul ; 2010 , Pages 4287-4290 ; 10514651 (ISSN) ; 9780769541099 (ISBN)
  9. URL: http://ieeexplore.ieee.org/document/5597754