Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matrix computations (3rd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of distance metrics for recognition based on non-negative matrix factorization
Pattern Recognition Letters
Introducing a weighted non-negative matrix factorization for image classification
Pattern Recognition Letters
Non-negative matrix factorization based methods for object recognition
Pattern Recognition Letters
Frontal face authentication using discriminating grids withmorphological feature vectors
IEEE Transactions on Multimedia
Frontal face authentication using morphological elastic graph matching
IEEE Transactions on Image Processing
Multiplicative updates for non-negative projections
Neurocomputing
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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.