@article{bdecb06f7262480aaba34a58a2b5f193,
title = "Real-time Facial Expression Recognition via Dense & Squeeze-and-Excitation Blocks",
abstract = "Due to the coronavirus disease 2019 (COVID-19) pandemic, traditional face-to-face courses have been transformed into online and e-learning courses. Although online courses provide flexible teaching and learning in terms of time and place, teachers cannot be fully aware of their students{\textquoteright} individual learning situation and emotional state. The cognition of learning emotion with facial expression recognition has been a vital issue in recent years. To achieve affective computing, the paper presented a fast recognition model for learning emotions through Dense Squeeze-and-Excitation Networks (DSENet), which rapidly recognizes students{\textquoteright} learning emotions, while the proposed real-time online feedback system notifies teacher instantaneously. Firstly, DSENet is trained and validated by an open dataset called Facial Expression Recognition 2013. Then, we collect students{\textquoteright} learning emotions from e-learning classes and apply transfer learning and data augmentation techniques to improve the testing accuracy.",
keywords = "Affective computing, E-learning, Emotion recognition, Facial expression recognition, Transfer learning",
author = "Tseng, {Fan Hsun} and Cheng, {Yen Pin} and Yu Wang and Suen, {Hung Yue}",
note = "Funding Information: This work is partly supported by the Young Scholar Fellowship Program under the auspices of the Ministry of Science & Technology (MOST) in Taiwan (Grant No. MOST109-2636-E-003-001), with partial funding from MOST in Taiwan (Grant No. MOST109-2511-H-003-046, MOST110-2222-E-006-011). Publisher Copyright: {\textcopyright} This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.",
year = "2022",
doi = "10.22967/HCIS.2022.12.039",
language = "English",
volume = "12",
journal = "Human-centric Computing and Information Sciences",
issn = "2192-1962",
publisher = "Springer Science + Business Media",
}