Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design

TL;DR

This paper introduces Mini-ResEmoteNet, a lightweight facial emotion recognition model developed via knowledge distillation, which improves efficiency and maintains high accuracy for usability testing applications.

Contribution

The study presents a novel knowledge distillation approach to create smaller, efficient emotion recognition models that outperform existing methods in accuracy and resource usage.

Findings

Mini-ResEmoteNet achieves 76.33% accuracy on FER2013.

The models show improved inference speed and reduced memory usage.

The approach surpasses state-of-the-art emotion recognition methods.

Abstract

Facial Emotion Recognition has emerged as increasingly pivotal in the domain of User Experience, notably within modern usability testing, as it facilitates a deeper comprehension of user satisfaction and engagement. This study aims to extend the ResEmoteNet model by employing a knowledge distillation framework to develop Mini-ResEmoteNet models - lightweight student models - tailored for usability testing. Experiments were conducted on the FER2013 and RAF-DB datasets to assess the efficacy of three student model architectures: Student Model A, Student Model B, and Student Model C. Their development involves reducing the number of feature channels in each layer of the teacher model by approximately 50%, 75%, and 87.5%. Demonstrating exceptional performance on the FER2013 dataset, Student Model A (E1) achieved a test accuracy of 76.33%, marking a 0.21% absolute improvement over EmoNeXt. Moreover, the results exhibit absolute improvements in terms of inference speed and memory usage during inference compared to the ResEmoteNet model. The findings indicate that the proposed methods surpass other state-of-the-art approaches.