Facial micro-expression recognition based on motion magnification network and graph attention mechanism

Abstract

Micro-expression is extensively studied due to their ability to fully reflect individuals' genuine emotions. However, accurate micro-expression recognition is a challenging task due to the subtle motion of facial muscle. Therefore, this paper introduces a Graph Attention Mechanism-based Motion Magnification Guided Micro-Expression Recognition Network (GAM-MM-MER) to amplify delicate muscle motions and focus on key facial landmarks. First, we propose a Swin Transformer-based network for micro-expression motion magnification (ST-MEMM) to enhance the subtle motions in micro-expression videos, thereby unveiling imperceptible facial muscle movements. Then, we propose a graph attention mechanism-based network for micro-expression recognition (GAM-MER), which optimizes facial key area maps and prioritizes adjacent nodes crucial for mitigating the influence of noisy neighbors, while attending to key feature information. Finally, experimental evaluations conducted on the CASME II and SAMM datasets demonstrate the high accuracy and effectiveness of the proposed network compared to state-of-the-art approaches. The results of our network exhibit significant superiority over existing methods. Furthermore, ablation studies provide compelling evidence of the robustness of our proposed network, substantiating its efficacy in micro-expression recognition.

Keywords

• Micro-expression recognition
• Graph attention mechanism
• Facial motion magnification
• Transformer