Self-supervised Learning for Expression Recognition on Small-scale Data Set

Abstract

In this paper, we propose a facial micro-expression recognition method utilizing self-supervised learning and Vision Transformer. We employ a contrast learning approach for self-supervision and extract image features using an attention mechanism. Various data augmentation techniques were utilized, and we specifically designed an enhancement method for facial recognition. By combining the strengths of the Vision Transformer and CNN models for feature extraction, our approach achieves improved recognition accuracy, even with limited labeled data. Experimental evaluation shows that our proposed method has good results in facial micro-expression recognition tasks.