ICASSP 2017accepted0 citations
Exemplar-embed complex matrix factorization for facial expression recognition
Viet-Hang Duong, Yuan-Shan Lee, Jian-Jiun Ding, Bach-Tung Pham, Manh-Quan Bui, Pham The Bao, Jia-Ching Wang
Abstract
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.
BibTeX
@inproceedings{icassp2017_exemplarembedcom,
title = {Exemplar-embed complex matrix factorization for facial expression recognition},
author = {Viet-Hang Duong and Yuan-Shan Lee and Jian-Jiun Ding and Bach-Tung Pham and Manh-Quan Bui and Pham The Bao and Jia-Ching Wang},
booktitle = {ICASSP 2017},
year = {2017}
}