@inproceedings{e169dfec79ce4dee89b49e895a9d5b03,
title = "Exemplar-embed complex matrix factorization for facial expression recognition",
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.",
keywords = "Complex matrix factorization, facial expression, nonnegative matrix factorization, optimization",
author = "Duong, {Viet Hang} and Lee, {Yuan Shan} and Ding, {Jian Jiun} and Pham, {Bach Tung} and Bui, {Manh Quan} and Bao, {Pham The} and Wang, {Jia Ching}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",
year = "2017",
month = jun,
day = "16",
doi = "10.1109/ICASSP.2017.7952474",
language = "???core.languages.en_GB???",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1837--1841",
booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",
}