Subclass discriminant Nonnegative Matrix Factorization for facial image analysis

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摘要

Nonnegative Matrix Factorization (NMF) is among the most popular subspace methods, widely used in a variety of image processing problems. Recently, a discriminant NMF method that incorporates Linear Discriminant Analysis inspired criteria has been proposed, which achieves an efficient decomposition of the provided data to its discriminant parts, thus enhancing classification performance. However, this approach possesses certain limitations, since it assumes that the underlying data distribution is unimodal, which is often unrealistic. To remedy this limitation, we regard that data inside each class have a multimodal distribution, thus forming clusters and use criteria inspired by Clustering based Discriminant Analysis. The proposed method incorporates appropriate discriminant constraints in the NMF decomposition cost function in order to address the problem of finding discriminant projections that enhance class separability in the reduced dimensional projection space, while taking into account subclass information. The developed algorithm has been applied for both facial expression and face recognition on three popular databases. Experimental results verified that it successfully identified discriminant facial parts, thus enhancing recognition performance.

论文关键词:Nonnegative Matrix Factorization,Subclass discriminant analysis,Multiplicative updates,Facial expression recognition,Face recognition

论文评审过程:Received 4 October 2011, Revised 21 March 2012, Accepted 26 April 2012, Available online 16 May 2012.

论文官网地址:https://doi.org/10.1016/j.patcog.2012.04.030