@article{aa124053dc504c318864f30192c83397,
title = "Maximum volume constrained graph nonnegative matrix factorization for facial expression recognition",
abstract = "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.",
keywords = "Facial expression recognition, Feature extraction, Graph regularized, Nonnegative matrix factorization, Projected gradient descent",
author = "Duong, {Viet Hang} and Bui, {Manh Quan} and Ding, {Jian Jiun} and Pham, {Bach Tung} and Bao, {Pham The} and Wang, {Jia Ching}",
note = "Publisher Copyright: Copyright {\textcopyright} 2017 The Institute of Electronics, Information and Communication Engineers.",
year = "2017",
month = dec,
doi = "10.1587/transfun.E100.A.3081",
language = "???core.languages.en_GB???",
volume = "E100A",
pages = "3081--3085",
journal = "IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences",
issn = "0916-8508",
number = "12",
}