Learning Associative Representation for Facial Expression Recognition

The main inherent challenges with the Facial Expression Recognition (FER) are high intra-class variations and high inter-class similarities, while existing methods pay little attention to the association within inter- and intra-class expressions. This paper introduces a novel Expression Associative Network (EAN) to learn association of facial expression, specifically, from two aspects: 1) associative topological relation over mini-batch is constructed by similarity matrix with an adjacent regularization, and 2) learning association of expressions with Graph Convolutional Network (GCN). Besides, an auxiliary module as invariant feature generator based on Generative Adversarial Networks (GAN) is designed to suppress pose variations, illumination changes, and occlusions. Results on public benchmarks achieve comparable or better performance compared with current state-of-the-art methods, with 90.07% on FERPlus, 86.36% on RAF-DB, and improve by 3.92% over SOTA on synthetic wrong labeling datasets.