A Self-Supervised Diffusion Framework For Facial Emotion Recognition

Abstract

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In this paper, we introduced a novel Facial Emotion Recognition (FER) framework that utilizes a diffusion-based approach and an attention mechanism. The model is efficiently trained through self-supervised learning, leveraging labeled and unlabelled data. The proposed framework has been rigorously tested on the FER2013 and AffectNet datasets, achieving promising accuracies of $67.2 \%$ and $68.1 \%$, respectively. The quantitative results not only surpass the performance of existing state-of-the-art FER models but also demonstrate the synergistic effect of combining diffusion-based modeling with self-supervised learning and attention mechanisms within a solid architectural framework. Our approach sets a new benchmark in the field, offering a significant step forward in the accurate and efficient recognition of facial expressions.