@inproceedings{7ded8039c5f540fbb7ac5341aa9d6d04,
title = "Facial Expression Recognition Using Sparse Complex Matrix Factorization with Ridge Term Regularization",
abstract = "This work proposes a novel method of matrix factorization on the complex domain to obtain both extracted features and coefficient matrix with high recognition results in facial expression recognition. The real data matrix is transformed into a complex number based on the Euler representation of complex numbers. Sparse regularization in dimensionality reduction using ridge term (L2-norm) is applied into this study. Basic complex matrix factorization (CMF) is modified into sparse complex matrix factorization using ridge term (SCMF-L2) which adding sparse L2-norm constraint in the coefficient. The gradient descent method is used to solve optimization problems. Experiments on facial expression recognition scenario reveal that the proposed methods provide better recognition results that prevalent NMF and CMF methods.",
keywords = "complex matrix factorization, facial expression recognition, feature extraction, non-negative matrix factorization",
author = "Putri, {Diyah Utami Kusumaning} and Aina Musdholifah and Faizal Makhrus and Duong, {Viet Hang} and Phuong, {Le Thi} and Chen, {Bo Wei} and Wang, {Jia Ching}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 10th IEEE Global Conference on Consumer Electronics, GCCE 2021 ; Conference date: 12-10-2021 Through 15-10-2021",
year = "2021",
doi = "10.1109/GCCE53005.2021.9621871",
language = "???core.languages.en_GB???",
series = "2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "45--46",
booktitle = "2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021",
}