Fast and Accurate Facial Expression Image Classification and Regression Method Based on Knowledge Distillation

Abstract

As emotional states are diverse, simply classifying them through discrete facial expressions has its limitations. Therefore, to create a facial expression recognition system for practical applications, not only must facial expressions be classified, emotional changes must be measured as continuous values. Based on the knowledge distillation structure and the teacher-bounded loss function, we propose a method to maximize the synergistic effect of jointly learning discrete and continuous emotional states of eight expression classes, valences, and arousal levels. The proposed knowledge distillation model uses Emonet, a state-of-the-art continuous estimation method, as the teacher model, and uses a lightweight network as the student model. It was confirmed that performance degradation can be minimized even though student models have multiply-accumulate operations of approximately 3.9 G and 0.3 G when using EfficientFormer and MobileNetV2, respectively, which is much less than the amount of computation required by the teacher model (16.99 G). Together with the significant improvements in computational efficiency (by 4.35 and 56.63 times using EfficientFormer and MobileNetV2, respectively), the decreases in facial expression classification accuracy were approximately 1.35% and 1.64%, respectively. Therefore, the proposed method is optimized for application-level interaction systems in terms of both the amount of computation required and the accuracy.

Revolutionizing online education: Advanced facial expression recognition for real-time student progress tracking via deep learning model

“…Initially, we evaluated the model's performance against FER-2013 to measure its resilience. Our proposed model demonstrated superior performance compared to models labeled as [34,[58][59][60][61][62][63][64], and [3], achieving accuracy improvements of 25 73.00% [3] 73.40% [56] 70.00% [57] 64.70% Model in this paper 91.71%…”

“…6.29%, and 8.6% when compared to models referenced as [3,34,61,63,65], and [27] respectively. We evaluated the model's robustness by examining its performance with the KDEF dataset.…”

“…In [34], the authors presented a novel approach for facial expression image classification and regression tasks. The study introduces a method that leverages knowledge distillation to achieve both speed and accuracy in facial expression analysis.…”

“…The findings indicated that our model achieved greater accuracy than the method presented by [66], surpassing it by 3.68%. Further investigation revealed that [34,67,68], and [63] By addressing these key factors and optimizing each aspect of the model's development and training pipeline, the ResNet50, CBAM, and TCNs model can achieve the highest accuracy in enhancing facial expression recognition tasks.…”

Enhancing Facial Expression Recognition System in Online Learning Context Using Efficient Deep Learning Model

“…In Kunyoung Lee et al's study [20], the authors presented a novel approach aimed at optimizing the combined learning of discrete and continuous emotional states within eight distinct expression classes, encompassing valences and arousal levels. Their proposed knowledge distillation model leverages Emonet, a cuttingedge continuous estimation method, as the teacher model, while employing a lightweight network as the student model.…”