A Novel Discriminant Non-Negative Matrix Factorization and its Application to Facial Expression Recognition

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.

• Periodical: Advanced Materials Research (Volumes 143-144)
• Pages: 129-133
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.143-144.129
• Online since: October 2010
• Authors: Yan Li Zhu, Jun Chen, Pei Xin Qu
• Keywords: Discriminant Constraint, Facial Expression Recognition, Low-Dimensional Feature, Non-Negative Matrix Factorization

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