Abstract
The technology of facial expression recognition is a challenging problem in the field of intelligent human-computer interaction. An algorithm based on the Gabor wavelet transformation and non-negative matrix factorization (G-NMF) is presented. The main process includes image preprocessing, feature extraction and classification. At first, the face region containing emotional information is obtained and normalized. Then, expressional features are extracted by Gabor wavelet transformation and the high-dimensional data are reduced by non-negative matrix factorization (NMF). Finally, two-layer classifier (TLC) is designed for expression recognition. Experiments are done on JAFFE facial expressions database. The results show that the method proposed has a better performance.
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Jun Zhou, professor. Born in 1966. Received M. Sc. Degrees from the Northeast Normal University and received Ph.D. degrees from the Northeastern University. Presently, she is a professor in Liaoning University of Technology. Scientific interest: artificial intelligence, data mining, and image processing.
Hongyan Mei. Born in 1978. Received M. Sc. degrees from the Liaoning University of Technology and received Ph. D. degrees from the Beijing University of Posts and Telecommunications. Scientific interest: data mining and big data analysis.
Sue Zhang. Born in 1987. Graduate from Xinzhou Teacher University, in 2007. Presently, she is a postgraduate at School of Electronics and Information Engineering, Liaoning University of Technology. Scientific interest: image processing and pattern recognition.
Dawei Wang. Born in 1988. Graduated from Shan Dong Jiaotong University. Presently, he is a postgraduate at School of Electronics and Information Engineering, Liaoning University of Technology. Scientific interest: image processing, data mining and big data analysis.
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Zhou, J., Zhang, S., Mei, H. et al. A method of facial expression recognition based on Gabor and NMF. Pattern Recognit. Image Anal. 26, 119–124 (2016). https://doi.org/10.1134/S1054661815040070
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DOI: https://doi.org/10.1134/S1054661815040070