Abstract
In the field of facial expression recognition, deep learning has attracted more and more researchers’ attention as a powerful tool. The method can effectively train and test data by using a neural network. This paper mainly uses the semi-supervised deep learning model for feature extraction and adds a regularized sparse representation model as a classifier. The combination of deep learning features and sparse representations fully exploits the advantages of deep learning in feature learning and the advantages of sparse representation in recognition. Experiments show that the features obtained by deep learning have certain subspace features, which accord with the subspace hypothesis of face recognition based on sparse representation. The method of this paper has a good recognition accuracy in facial expression recognition and has certain advantages in small sample problems.
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Acknowledgements
This paper is supported by Jilin Provincial Education Department “13th five-year” Science, Technology Project (No. JJKH20170571KJ), National Natural Science Foundation of China under Grant 61873304, The Science & Technology Plan Project Changchun City under Grant No. 17SS012, and the Industrial Innovation Special Funds Project of Jilin Province under Grant No. 2018C038-2 & 2019C010.
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Liu, T., Guo, W., Sun, Z., Lian, Y., Liu, S., Wu, K. (2020). Facial Expression Recognition Based on Regularized Semi-supervised Deep Learning. In: Pan, JS., Li, J., Tsai, PW., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 157. Springer, Singapore. https://doi.org/10.1007/978-981-13-9710-3_34
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DOI: https://doi.org/10.1007/978-981-13-9710-3_34
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