Facial Motion Prior Networks for Facial Expression Recognition
This work addresses facial expression recognition for applications like human-computer interaction, but it is incremental as it builds on existing deep learning methods by adding domain knowledge.
The authors tackled the problem of facial expression recognition by proposing a novel framework that incorporates facial motion priors to focus on muscle moving regions, achieving state-of-the-art results on three benchmark datasets.
Deep learning based facial expression recognition (FER) has received a lot of attention in the past few years. Most of the existing deep learning based FER methods do not consider domain knowledge well, which thereby fail to extract representative features. In this work, we propose a novel FER framework, named Facial Motion Prior Networks (FMPN). Particularly, we introduce an addition branch to generate a facial mask so as to focus on facial muscle moving regions. To guide the facial mask learning, we propose to incorporate prior domain knowledge by using the average differences between neutral faces and the corresponding expressive faces as the training guidance. Extensive experiments on three facial expression benchmark datasets demonstrate the effectiveness of the proposed method, compared with the state-of-the-art approaches.