Improving Generalization of Reinforcement Learning Using A Bilinear Policy Network

Facial expression recognition (FER) has made significant progress over the past few years. But how to overcome the problem of high inter-class similarity and large intra-class difference in FER is still challenging. To address this problem, we propose a novel FER framework called AU-assisted Visual Transformer (AVT) by incorporating facial action units (AU) information into Visual Transformer, which mainly consists of three modules: Local Feature Extraction (LFE) module, Global Relationship Modeling (GRM) module and AU Fusion Module (AFM). Specifically, the LFE module aims to extract local facial expression features by using a deep convolutional neural network, the GRM module is a multi-layer Transformer encoder that captures the relation between local facial regions and obtains a global understanding of the face, and in particular, the AFM introduces fine-grained AU feature and fuses it with expression feature for final classification. Extensive experiments are conducted on RAF-DB and FERPlus datasets, and our AVT achieves competitive results compared to previous state-of-the-art methods, demonstrating the effectiveness of our approach.