Facial expression recognition based on multi-task learning with spatial feature disentanglement

Huawen Chen; Ruirui Ji; Zhilian Wang

doi:10.1117/12.2589355

27 January 2021 Facial expression recognition based on multi-task learning with spatial feature disentanglement

Huawen Chen, Ruirui Ji, Zhilian Wang

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 117201M (2021) https://doi.org/10.1117/12.2589355
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

Most presented facial expression recognition methods laid more emphasis on the facial features extracted from expression images, but ignored the coupled relationship between facial expression features and identity features. This paper proposes a novel expression recognition method based on spatial feature disentanglement. Expression features and identity features are encoded with deep neural network independently under a multi-task framework. A latent space discriminator is designed to disentangle spatial features and weaken the impact of identity features on expression recognition. The experimental identification accuracy on CK+ and RaFD datasets could achieve 99.69% and 97.64% respectively, which verifies that the proposed method has better generalization ability and strong robustness.

Citation Download Citation

Huawen Chen, Ruirui Ji, and Zhilian Wang "Facial expression recognition based on multi-task learning with spatial feature disentanglement", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 117201M (27 January 2021); https://doi.org/10.1117/12.2589355

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