用多尺度网络+对比学习实现实时人脸情绪变化检测
A Multiscale Network with Supervised Contrastive Learning for Real-Time Facial Emotion Recognition
- 构建多尺度网络捕捉表情变化的局部与全局特征
- 在标准数据集上实现高精度实时情绪识别
- 适合心理学辅助、人机交互等需要情感感知的场景
从面部表情进行实时情绪识别是一项具有挑战性的任务，尤其在视频场景中，情绪状态随时间持续变化且个体差异显著。面部表情的变化是连续而非离散的，难以通过计算手段准确建模。本文提出一种基于深度学习的系统，通过建模面部表情的动态变化，实现实时视频中情绪状态的检测。该方法在标准数据集上训练并取得良好性能，具备在心理咨询等场景中为心理医生提供额外情绪洞察的潜力。
原文摘要 · Abstract (English)
Real-time emotion recognition from facial expressions is a challenging task, particularly in video-based scenarios where multiple emotional states may occur over time. The difficulty increases further due to the fact that each emotional state is associated with facial expressions that vary significantly across individuals. The change of facial expressions portraying emotional state is not discrete, but rather continuous, which is very challenging to represent through computational aids. A system with the ability to detect variations in facial expressions can have a significant impact on determining the emotional state of an individual. Such a system can be very beneficial for psychologists during counseling by providing additional insights into the emotional state of a subject. In this paper, a deep learning-based system is presented to detect emotional changes in real-time video of a person by modeling the change in facial expressions. The current study is conducted on a standard dataset for training of the deep learning system and the system has provided very satisfactory outcomes in this respect.
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