A Novel Discriminant Non-Negative Matrix Factorization and its Application to Facial Expression Recognition
Authors:
- 1 October 2010
- journal article
- Published by Trans Tech Publications, Ltd. in Advanced Materials Research
- Vol. 143-144, 129-133
Abstract
The paper proposes a novel discriminant non-negative matrix factorization algorithm and applies it to facial expression recognition. Unlike traditional non-negative matrix factorization algorithms, the algorithm adds discriminant constraints in low-dimensional weights. The experiments on facial expression recognition indicate that the algorithm enhances the discrimination capability of low-dimensional features and achieves better performance than other non-negative matrix factorization algorithms.Keywords
This publication has 13 references indexed in Scilit:
- Time-frequency Spectra Recognition Based on Sparse Non-negative Matrix Factorization and Support Vector MachineActa Automatica Sinica, 2009
- Non-negative matrix factorization methods for face recognition under extreme lighting variations2009
- Nonnegative Matrix Factorization with Earth Mover's Distance metric2009
- Doubly weighted nonnegative matrix factorization for imbalanced face recognition2009
- Algorithms for orthogonal nonnegative matrix factorization2008
- Projected Gradient Methods for Nonnegative Matrix FactorizationNeural Computation, 2007
- NMF, LNMF, and DNMF modeling of neural receptive fields involved in human facial expression perceptionJournal of Visual Communication and Image Representation, 2006
- Application of non-negative and local non negative matrix factorization to facial expression recognition2004
- Learning the parts of objects by non-negative matrix factorizationNature, 1999
- Eigenfaces for RecognitionJournal of Cognitive Neuroscience, 1991