Attention-Guided Self-supervised Framework for Facial Emotion Recognition

Facial expression recognition is pivotal in computer vision and finds applications across various domains. In this paper, we proposed a self-supervised learning approach for precise facial expression recognition. Our approach leverages recent advancements in diffusion models, specifically the Classification and Regression Diffusion (CARD) model. To enhance the discriminative capability of our model, we integrate the Convolutional Block Attention Module (CBAM), an effective attention mechanism, to extract pertinent and discriminative feature maps. Furthermore, we capitalize on unlabelled data by using the simple contrastive learning framework of self-supervised learning (SSL) to extract meaningful features. To evaluate the performance, we conduct extensive experiments on the FER2013 dataset, comparing our results with existing benchmarks. The findings reveal significant performance improvements, achieving 66.6% accuracy on the FER2013 dataset. The quantitative results demonstrate the efficacy of our proposed SSL-based model in achieving accurate and robust facial expression recognition.