A novel iris segmentation using radial-suppression edge detection
Signal Processing
Discriminant nonnegative tensor factorization algorithms
IEEE Transactions on Neural Networks
Face recognition under varying illumination using gradientfaces
IEEE Transactions on Image Processing
A doubly weighted approach for appearance-based subspace learning methods
IEEE Transactions on Information Forensics and Security
Nonnegative matrix factorization with bounded total variational regularization for face recognition
Pattern Recognition Letters
Nonlinear nonnegative matrix factorization based on Mercer kernel construction
Pattern Recognition
Extracting non-negative basis images using pixel dispersion penalty
Pattern Recognition
Subclass discriminant Nonnegative Matrix Factorization for facial image analysis
Pattern Recognition
Multiple kernel local Fisher discriminant analysis for face recognition
Signal Processing
Measuring the degree of face familiarity based on extended NMF
ACM Transactions on Applied Perception (TAP)
Face recognition using Weber local descriptors
Neurocomputing
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In this paper, a novel topology preserving non-negative matrix factorization (TPNMF) method is proposed for face recognition. We derive the TPNMF model from original NMF algorithm by preserving local topology structure. The TPNMF is based on minimizing the constraint gradient distance in the high-dimensional space. Compared with L2 distance, the gradient distance is able to reveal latent manifold structure of face patterns. By using TPNMF decomposition, the high-dimensional face space is transformed into a local topology preserving subspace for face recognition. In comparison with PCA, LDA, and original NMF, which search only the Euclidean structure of face space, the proposed TPNMF finds an embedding that preserves local topology information, such as edges and texture. Theoretical analysis and derivation given also validate the property of TPNMF. Experimental results on three different databases, containing more than 12 000 face images under varying in lighting, facial expression, and pose, show that the proposed TPNMF approach provides a better representation of face patterns and achieves higher recognition rates than NMF.