Emotic Masked Autoencoder with Attention Fusion for Facial Expression Recognition

arXiv:2403.13039v1 Announce Type: new
Abstract: Facial Expression Recognition (FER) is a critical task within computer vision with diverse applications across various domains. Addressing the challenge of limited FER datasets, which hampers the generalization capability of expression recognition models, is imperative for enhancing performance. Our paper presents an innovative approach integrating the MAE-Face self-supervised learning (SSL) method and Fusion Attention mechanism for expression classification, particularly showcased in the 6th Affective Behavior Analysis in-the-wild (ABAW) competition. Additionally, we propose preprocessing techniques to …

abstract applications arxiv attention autoencoder capability challenge computer computer vision cs.cv datasets diverse diverse applications domains fusion masked autoencoder paper performance recognition type vision