Multi-Attribute Feature-Aware Network for Facial Expression Recognition

Wei Yen Hsu, Yu Chieh Chen

Abstract: Facial expression recognition (FER) has gained popularity as a research topic due to its broad applicability. However, real-world environments present significant challenges to FER, including occlusion, illumination variation, and angle. To address these issues, we propose a novel multi-Attribute feature-Aware network (MAFaNet) to enhance the performance and accuracy of FER in real-world environments. The proposed MAFaNet enhances the FER performance in real-world environments by effectively utilizing multi-Attribute facial features from global, local, and salient subregions and thus fully exploiting the diverse potential information provided by each facial attribute, in line with the human face perception mechanism that extracts both global and regional information. Specifically, the global facial feature (GFF) module focuses on the more important facial features in the overall face by expanding the number of channels to preserve features and assigning weights to different channels. Moreover, the local facial feature (LFF) and salient facial feature (SFF) modules capture regional feature information from local and salient facial features, respectively, focusing on fine-grained regional features and reducing the interference from irrelevant regions in feature extraction. The experimental results indicate that the proposed MAFaNet method achieves the promising FER performance in comparison with the state-of-The-Art approaches on several real-world datasets.

External IDs:doi:10.1145/3735559