Facial Attention based Convolutional Neural Network for 2D3D Facial Expression Recognition Discriminative facial parts are essential for facial expression recognition (FER) tasks because of small interclass differences and large intraclass variations in expression images. Existing methods localize discriminative regions with the aid of extra facial landmarks, such as action units (AU). However, it consumes a lot of manpower in manually labeling. To address this problem, in this paper, we propose an advanced facial attention based convolutional neural network (FACNN) for 2D3D FER. The main contribution of FACNN is the facial attention mechanism, which enables the network to localize the discriminative regions automatically from multimodality expression images without dense landmark annotations. Experimental results conducted on BU3DFE demonstrate that FACNN achieves stateoftheart performance comparing with the existing 2D3D FER techniques, and the discriminative facial parts estimated by the facial attention mechanism are highly interpretable and consistent with human perception.

Facial Motion Prior Networks for Facial Expression Recognition 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 stateoftheart approaches.