Discriminative Attention-based Convolutional Neural Network for 3D Facial Expression Recognition

Abstract

3D Facial Expression Recognition (FER) is an active research area in computer vision. Although previous methods report promising results, two key issues still remain to be solved. On the one hand, different facial areas contribute unequally to performing various expressions, but most existing methods extract features from the entire 3D surface. On the other hand, the difference between expressions varies, while previous methods generally treat different emotions equally, making some of them extremely hard to be distinguished. To solve these problems, we propose a novel approach for 3D FER, namely Discriminative Attention-based Convolution Neural Network (DA-CNN), to generate more comprehensive expression related representations. DA-CNN introduces an attention module to the CNN models, which helps the deep model selectively focus on emotional salient regions in a learnable way. Furthermore, a novel loss named Dimensional Distribution (DD) loss is proposed to model the inter-expression relationship. Supervised by DD loss, DA-CNN can generate more discriminative expression representation. Extensive experiments are conducted on BU-3DFE dataset, and the results show that DA-CNN achieves significant improvement over the state-of-the-art.