Class-Specific Discriminant Non-negative Matrix Factorization for Frontal Face Verification

In this paper, a supervised feature extraction method having both non-negative bases and weights is proposed. The idea is to extend the Non-negative Matrix Factorization (NMF) algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. The proposed method incorporates discriminant constraints inside the NMF decomposition in a class specific manner. Thus, a decomposition of a face to its discriminant parts is obtained and new update rules for both the weights and the basis images are derived. The introduced methods have been applied to the problem of frontal face verification using the well known XM2VTS database. The proposed algorithm greatly enhance the performance of NMF for frontal face verification.