A self-attention-based fusion framework for facial expression recognition in wavelet domain.

Facial expression recognition (FER) plays a vital role for applications based on human–computer interaction. In the past few years, many deep learning models have been proposed for FER, but their performance is limited due to challenges such as variation in head pose, occlusion, illumination, etc. Moreover, existing models consider input image through holistic view without giving attention to features relevant to expressions. In this work, we propose a deep fusion framework for FER which employs self-attention mechanism to resolve this issue. Furthermore, to improve feature representation of the images, the proposed model transforms the input image to wavelet domain through discrete wavelet transform. The framework employs two parallel branches for shallow and deep features which are fused for better feature representation. The proposed model is evaluated on posed and in-the-wild datasets: CK+, JAFFE, MUG, YALE, RAF, and experimental results validate the effectiveness of the model. [ABSTRACT FROM AUTHOR]