@inproceedings{d23088dd0d064364838edceaaff2fe98,
title = "Facial expression recognition algorithm based on equal probability symbolization entropy",
abstract = "Electroencephalogram (EEG) records brain activity using electrophysiological markers and is a comprehensive representation of the dynamic activity of human brain neurons. EEG can be used to study human facial expression recognition. In fact, entropy values of EEG can fully reflect changes in facial expressions. This paper improves the sample entropy and the permutation entropy by introducing equal probability symbolization and applies the equal probability symbolization entropy to facial expression recognition. The original permutation entropy, sample entropy and equal-probability symbolization entropy values are calculated for the three expressions of anger, fear and happiness. The results demonstrate that equal-probability symbolization entropy can distinguish human facial expressions clearly and accurately.",
keywords = "EEG signal, Equal probability symbolization entropy, Facial expression recognition",
author = "Fa Zheng and Bin Hu and Xiangwei Zheng",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2019.; 13th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2018 ; Conference date: 18-08-2018 Through 19-08-2018",
year = "2019",
doi = "10.1007/978-981-13-3044-5_34",
language = "English",
isbn = "9789811330438",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "469--477",
editor = "Xiaolan Xie and Yuqing Sun and Tun Lu and Hongfei Fan and Liping Gao",
booktitle = "Computer Supported Cooperative Work and Social Computing - 13th CCF Conference, ChineseCSCW 2018, Revised Selected Papers",
address = "Germany",
}