用扩散模型生成人脸表情数据，显著提升情绪识别准确率。
Improvement in Facial Emotion Recognition using Synthetic Data Generated by Diffusion Model
- 用Stable Diffusion生成合成表情数据，缓解数据不平衡问题。
- 在FER2013和RAF-DB上分别达到96.47%和99.23%准确率。
- 适合关注生成数据增强与情绪识别的研究者。
面部情绪识别（FER）在人机交互、情感计算、心理健康监测和个性化学习等领域具有重要意义。然而，现有数据集普遍存在类别不平衡问题，制约了模型性能与泛化能力。本文通过引入合成数据增强，并结合ResEmoteNet模型，提升面部情绪识别性能。采用Stable Diffusion 2与Stable Diffusion 3 Medium模型生成合成面部表情数据，对FER2013与RAF-DB基准数据集进行训练集扩充。使用增强后数据训练ResEmoteNet，分别在FER2013和RAF-DB上取得96.47%和99.23%的准确率，较原结果提升16.68%和4.47%，验证了合成数据增强的有效性，凸显先进生成模型在FER研究中的潜力。ResEmoteNet源码已公开于https://github.com/ArnabKumarRoy02/ResEmoteNet。
原文摘要 · Abstract (English)
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
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