Learning informative and discriminative semantic features for robust facial expression recognition

Abstract

Facial expression recognition (FER) becomes challenging in real-world scenarios, which requires learning informative and discriminative features from challenging datasets to obtain robust facial expression recognition. In this paper, we propose an Informative and Discriminative Semantic Features Learning (IDSFL) network for FER against occlusion and head pose in the wild. Specifically, IDSFL aims to mine informative and discriminative semantic features from both low and high levels learned features to learn robust representations. First, a multi-channel feature (MCF) modulator incorporating low-level Gabor features is introduced to learn informative semantic features by capturing adequate diverse and detailed information. Additionally, a specific emotion-aware (SEA) module is proposed to learn discriminative semantic features by aggregating high-level emotion-specific features to focus on each expression category. Thus, IDSFL can collaboratively learn informative and discriminative representations. Extensive experiments on challenging in-the-wild datasets, including RAF-DB, FERPlus and AffectNet-7, demonstrate that our proposed method outperforms most state-of-the-art FER methods.