Agent-Poster: A Multi-Scale Feature Fusion Emotion Recognition Model Based on an Agent Attention Mechanism

Facial expression recognition (FER) serves as a pivotal approach for understanding human affective states and behavioral intentions, forming the fundamental basis for achieving natural interaction in affective computing systems. To address the limitations of convolutional neural networks in capturing global facial expression features, while simultaneously overcoming the challenges of Vision Transformers regarding their substantial parameter requirements, high computational complexity, and difficulties in meeting lightweight deployment demands for practical applications, this paper proposes Agent-Poster, a lightweight multi-scale facial expression recognition model based on Agent Attention. Building upon the POSTER++ framework, the model innovatively integrates Agent Attention, adopts a streamlined dual-stream architecture to minimize redundant interactions, and implements efficient multi-scale feature fusion. Experimental results demonstrate that the proposed method achieves superior recognition performance compared to existing approaches, attaining accuracy rates of 92.61% on the RAF-DB dataset and 68.21% on the AffectNet dataset, thereby validating its robustness and accuracy in facial expression recognition tasks.