Maximum Volume Constrained Graph Nonnegative Matrix Factorization for Facial Expression Recognition

Viet-Hang DUONG  Manh-Quan BUI  Jian-Jiun DING  Bach-Tung PHAM  Pham The BAO  Jia-Ching WANG  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E100-A   No.12   pp.3081-3085
Publication Date: 2017/12/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E100.A.3081
Type of Manuscript: LETTER
Category: Image
facial expression recognition,  nonnegative matrix factorization,  feature extraction,  graph regularized,  projected gradient descent,  

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In this work, two new proposed NMF models are developed for facial expression recognition. They are called maximum volume constrained nonnegative matrix factorization (MV_NMF) and maximum volume constrained graph nonnegative matrix factorization (MV_GNMF). They achieve sparseness from a larger simplicial cone constraint and the extracted features preserve the topological structure of the original images.