Improvement in Facial Emotion Recognition using Synthetic Data Generated by Diffusion Model
This work addresses data imbalance for facial emotion recognition applications like human-computer interaction and mental health monitoring, representing an incremental improvement through synthetic data augmentation.
The paper tackled class imbalance in facial emotion recognition datasets by using synthetic data generated with Stable Diffusion models to augment training sets, achieving accuracies of 96.47% on FER2013 and 99.23% on RAF-DB with absolute improvements of 16.68% and 4.47%, respectively.
Facial Emotion Recognition (FER) plays a crucial role in computer vision, with significant applications in human-computer interaction, affective computing, and areas such as mental health monitoring and personalized learning environments. However, a major challenge in FER task is the class imbalance commonly found in available datasets, which can hinder both model performance and generalization. In this paper, we tackle the issue of data imbalance by incorporating synthetic data augmentation and leveraging the ResEmoteNet model to enhance the overall performance on facial emotion recognition task. We employed Stable Diffusion 2 and Stable Diffusion 3 Medium models to generate synthetic facial emotion data, augmenting the training sets of the FER2013 and RAF-DB benchmark datasets. Training ResEmoteNet with these augmented datasets resulted in substantial performance improvements, achieving accuracies of 96.47% on FER2013 and 99.23% on RAF-DB. These findings shows an absolute improvement of 16.68% in FER2013, 4.47% in RAF-DB and highlight the efficacy of synthetic data augmentation in strengthening FER models and underscore the potential of advanced generative models in FER research and applications. The source code for ResEmoteNet is available at https://github.com/ArnabKumarRoy02/ResEmoteNet