Exemplar-embed complex matrix factorization for facial expression recognition

This paper presents an image representation approach which is based on matrix factorization in the complex domain and called exemplar-embed complex matrix factorization (EE-CMF). The proposed EE-CMF approach can very effectively improve the performance of facial expression recognition. Moreover, Wir...

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Bibliographic Details
Main Authors: Viet-Hang Duong, Yuan-Shan Lee, Jian-Jiun Ding, Bach-Tung Pham, Manh-Quan Bui, Pham The Bao, Jia-Ching Wang
Format: Conference Proceeding
Language:English
Subjects:
Calculus
Complex matrix factorization
Face recognition
facial expression
Image recognition
Matrix decomposition
nonnegative matrix factorization
Optimization
Training
Online Access:Request full text
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Summary:This paper presents an image representation approach which is based on matrix factorization in the complex domain and called exemplar-embed complex matrix factorization (EE-CMF). The proposed EE-CMF approach can very effectively improve the performance of facial expression recognition. Moreover, Wirtinger's calculus was employed to determine derivatives. The gradient descent method was utilized to solve the complex optimization problem. Experiments on facial expression recognition verified the effectiveness of the proposed EE-CMF. It provides consistently better recognition results than standard NMFs.
ISSN:2379-190X
DOI:10.1109/ICASSP.2017.7952474