@inproceedings{9f96352b2f7f4e29875da39be1f12c3b,
title = "A new constrained nonnegative matrix factorization for facial expression recognition",
abstract = "A new NMF model, spatial constrained graph sparse nonnegative matrix factorization (SGSNMF) is adopted for facial expression recognition. In this model, the extracted features preserve the topological structure of the original images and achieve sparseness from L2 constraint on coefficient matrix, meanwhile the base satisfy pixel dispersion penalty. The proposed method takes advantage of the project gradient decent and is based on the alternating nonnegative least square framework. Experiments on two facial expression recognition scenarios that involve a whole face and an occluded face reveal that the proposed algorithm outperforms the prevalent NMF methods.",
keywords = "Facial expression recognition, Graph regularization, Nonnegative matrix factorization, Projectedgradient descent",
author = "Duong, {Viet Hang} and Bui, {Manh Quan} and Bao, {Pham The} and Wang, {Jia Ching}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 5th International Conference on Orange Technologies, ICOT 2017 ; Conference date: 08-12-2017 Through 10-12-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ICOT.2017.8336093",
language = "???core.languages.en_GB???",
series = "Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "79--82",
editor = "Minghui Dong and Lei Wang and Yanfeng Lu and Haizhou Li",
booktitle = "Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017",
}
