FCCA: Fast Center Consistency Attention for Facial Expression Recognition

Given the critical requirements for both speed and accuracy in facial expression recognition, this paper presents a novel deep-learning architecture named Fast Central Consistency Attention (FCCA). With FasterNet-s as its backbone network, FCCA is designed to recognize facial expressions. Firstly, we leverage partial convolution to extract features from specific channels, thereby reducing frequent memory access and substantially boosting training speed. Secondly, we enhance recognition accuracy by introducing an additional pointwise convolution on the partial features, focusing on the central facial position using weighted mechanisms. Lastly, we integrate flip consistency loss to tackle uncertainty challenges inherent in facial expression recognition (FER) tasks, further improving the overall model performance. Our approach yielded superior results: we achieved recognition accuracies of 91.30% on RAF-DB and 65.51% on AffectNet datasets, along with 56.61% UAR and 69.66% WAR on the DFEW dataset. The FCCA method has demonstrated state-of-the-art performance across multiple datasets, underscoring its robustness and capability for generalization.