Mini-ResEmoteNet: Leveraging Knowledge Distillation for Human-Centered Design
This work addresses the need for efficient emotion recognition in user experience testing, but it is incremental as it builds on the existing ResEmoteNet model with minor architectural modifications.
The study tackled the problem of creating lightweight facial emotion recognition models for usability testing by applying knowledge distillation to develop Mini-ResEmoteNet, achieving a test accuracy of 76.33% on the FER2013 dataset with a 0.21% improvement over EmoNeXt and enhancements in inference speed and memory usage.
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.