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

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.