Facial expression recognition using switch instance-batch normalization

Wenhao Tang

doi:10.1117/12.2586281

12 March 2021 Facial expression recognition using switch instance-batch normalization

Wenhao Tang

Proceedings Volume 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications; 117631H (2021) https://doi.org/10.1117/12.2586281
Event: Seventh Symposium on Novel Photoelectronic Detection Technology and Application 2020, 2020, Kunming, China

Abstract

Facial expression recognition is widely used in video surveillance, assisted driving, and distance education. With the application of deep learning technology in facial expression recognition, many previous studies have shown good performance, but they are mainly identified in small-scale datasets with limited samples, lacking generalization ability for a wide range of scenes, and can’t meet the practical application needs. Previous studies have shown that Instance Normalization (IN) exhibits strong performance in terms of appearance invariance. In this paper, the normalization layer of facial expression recognition network is studied extensively, and a Switchable Instance-Batch Normalization (SIBN) method is proposed to balance feature appearance variance and content semantic information. The method was verified in three commonly used expression datasets CK+, Oulu-CASIA, and MMI. The experimental results show that SIBN stability improves the recognition accuracy of the model on a single dataset, and greatly enhances the performance of network cross-domain identification. The experimental results demonstrate the superiority of the proposed method.

Citation Download Citation

Wenhao Tang "Facial expression recognition using switch instance-batch normalization", Proc. SPIE 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications, 117631H (12 March 2021); https://doi.org/10.1117/12.2586281

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