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.
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Acknowledgements
National Natural Science Foundation of China (61373149) and the Taishan Scholars Program of Shandong Province, China.
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Zheng, F., Hu, B., Zheng, X. (2019). Facial Expression Recognition Algorithm Based on Equal Probability Symbolization Entropy. In: Sun, Y., Lu, T., Xie, X., Gao, L., Fan, H. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2018. Communications in Computer and Information Science, vol 917. Springer, Singapore. https://doi.org/10.1007/978-981-13-3044-5_34
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DOI: https://doi.org/10.1007/978-981-13-3044-5_34
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