FERmc: Facial expression recognition framework based on multi-branch fusion and depthwise separable convolution

Facial expressions are often expressed through specific key areas of the face. Currently, facial expression recognition has garnered widespread attention and become a trending research topic in human–computer interaction, healthcare, and virtual reality. However, the inter-class similarity degree is rather high between different expressions, which may lead to difficulties in feature extraction and high computational complexity in classification. In this article, we introduce a new Facial Expression Recognition framework named FERmc, which is based on Multi-branch Fusion and Depthwise Separable Convolution. Specifically, we design a neural network with multiple convolutional branches to adaptively capture features with different scales from the images. By employing attention modules, the designed network can automatically focus on the most discriminative local regions on the images, thereby improving the robustness of feature representation. Moreover, FERmc can enhance the efficiency and effectiveness with the attention mechanism in feature extraction. To evaluate the performance of FERmc, extensive experiments are conducted over three facial expression recognition datasets. The performance analysis indicates that FERmc can achieve high performance and significantly outperform other benchmark algorithms, demonstrating the superiority and effectiveness of FERmc in recognition tasks for facial expression.