Abstract
Facial emotion recognition is important in many academic and industrial applications. In this paper, our team proposed a novel facial emotion recognition method. First, we used discrete wavelet transform to extract wavelet coefficients from facial images. Second, principal component analysis was utilized to reduce the features. Third, a single-hidden-layer neural network was used as the classifier. Finally and most importantly, we introduced the cat swarm optimization to train the weights and biases of the classifier. The ten-fold stratified cross validation showed cat swarm optimization method achieved an overall accuracy of 89.49 ± 0.76%. It was better than genetic algorithm, particle swarm optimization, and time-varying-acceleration-coefficient particle swarm optimization. Besides, our facial emotion recognition system was better than two state-of-the-art approaches.
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Acknowledgment
This paper is supported by Natural Science Foundation of China (61602250), Natural Science Foundation of Jiangsu Province (BK20150983), Program of Natural Science Research of Jiangsu Higher Education Institutions (16KJB520025, 15KJB470010), Key Laboratory of Measurement and Control of Complex Systems of Engineering, Southeast University, Ministry of Education (MCCSE2017A02).
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Wang, SH., Yang, W., Dong, Z., Phillips, P., Zhang, YD. (2017). Facial Emotion Recognition via Discrete Wavelet Transform, Principal Component Analysis, and Cat Swarm Optimization. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_18
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