JADFER: Exploring Spatial-Contextual Interaction With Joint Attention Dropping for Facial Expression Recognition

Abstract

Facial Expression Recognition (FER) aims to categorize emotional expressions depicted on a human face, and is a challenging task under unconstrained conditions, such as face occlusions and pose variations. Recent methods usually adopt self attention or cross attention to explore global or local relationships among different level features. However, these methods are inclined to focus on the redundant facial regions, causing model overfitting. To address this problem, we propose a new FER model named JADFER, which drops the joint attention in the weight matrix to adaptively enhance facial expression representations. Specifically, our JADFER model consists of three components: Spatial Branch (SB), Contextual Branch (CB), and Spatial-Contextual Interaction (SCI). First, SB runs $N$ paths in parallel, where a Variety loss is designed to guide the paths of SB to focus on different discriminative regions. Meanwhile, CB abstracts the contextual facial representations using self attention with Joint Attention Dropping (JAD). Then, the SCI adopts the spatial features from SB to query the contextual representations from CB through cross attention with JAD, which regulates the attention weights by dropping the similar activations to further enhance the facial embeddings. Experimental results demonstrate that the proposed model outperforms the state-of-the-art methods on several FER benchmarks.