Learning Discriminative Features with Region Attention and Refinement Network for Facial Expression Recognition in the Wild

Abstract

Facial Expression Recognition (FER) has achieved significant performance in recent years. However, learning discriminative expression features is still a pending issue, due to disturbances in the wild and high similarities across different expressions. To remedy this issue, we present an innovative region attention and refinement network (RARN) capable of learning discriminative and robust features for accurate FER in the wild. Specifically, RARN mainly learn discriminative features from two different aspects: Multi-head Local Attention Network (MLAN) and a Latent Feature Mining Network (LFMN). MLAN can accurately locate the regions of interest and extract robust features in the wild by employing two modules, in which the region generation module divides the midlevel feature maps into several subregions and local attention module highlights the local discriminative features of regions of interest through attention block. LFMN combines a series of latent expression features associated with their corresponding importance weights to further obtain the final discriminative expression features. Experimental results on three widely-used in-the-wild FER datasets demonstrate proposed algorithm achieves favorable performance against state-of-the-art methods. The source code will be found at https://github.com/lizoe/RARN.