Task-Aware Few-Shot Visual Classification With Improved Self-Supervised Metric Learning

in this study, we propose a video-based facial expression recognition method that utilizes the ?emotion-wheel model.? in research on emotions in the field of psychology, a model has been proposed in which basic emotions are arranged in a circular pattern, such as where ?happiness? and ?sadness? are opposites. Therefore, we utilized this knowledge as an inductive bias to improve accuracy by embedding features that are consistent with the emotion model in the process of class identification for recognizing facial expressions in videos. As a result of a performance evaluation using the CK+, MMI, and AFEW datasets, the recognition rates of the proposed method are 98.78%, 81.95%, and 55.31% for each dataset, which are 3.67%, 6.34%, and 4.9% higher than the baseline method, respectively. Furthermore, the proposed method outperforms the state-of-the-art method on MMI and AFEW.