融合深度特征与面部动作单元，实现情绪识别的持续学习。
Feature Aggregation for Efficient Continual Learning of Complex Facial Expressions
- 结合卷积特征与面部动作单元，构建混合表征。
- 在CFEE数据集上准确识别基础与复合表情，遗忘率显著降低。
- 适合需要长期适应用户情绪的智能系统开发者。
随着人工智能日益融入日常生活，识别并适应人类情绪对人机交互至关重要。面部表情识别（FER）是推断情感状态的主要途径，但情绪的动态性和文化差异要求模型具备持续学习能力且不遗忘旧知识。本文提出一种融合深度卷积特征与面部动作单元（AUs）的混合框架，用于持续学习场景下的FER，有效缓解灾难性遗忘。通过贝叶斯高斯混合模型（BGMM）建模联合表示，实现轻量化、概率化推理，无需重新训练即可保持强判别力。在复合情绪表达数据集（CFEE）上，模型可先学习基本表情，再逐步识别复杂复合表情。实验表明，该方法在准确率、知识保留和遗忘抑制方面均有提升。本框架为开发具有情感智能的AI系统提供了支持，适用于教育、医疗及自适应用户界面等场景。
原文摘要 · Abstract (English)
As artificial intelligence (AI) systems become increasingly embedded in our daily life, the ability to recognize and adapt to human emotions is essential for effective human-computer interaction. Facial expression recognition (FER) provides a primary channel for inferring affective states, but the dynamic and culturally nuanced nature of emotions requires models that can learn continuously without forgetting prior knowledge. In this work, we propose a hybrid framework for FER in a continual learning setting that mitigates catastrophic forgetting. Our approach integrates two complementary modalities: deep convolutional features and facial Action Units (AUs) derived from the Facial Action Coding System (FACS). The combined representation is modelled through Bayesian Gaussian Mixture Models (BGMMs), which provide a lightweight, probabilistic solution that avoids retraining while offering strong discriminative power. Using the Compound Facial Expression of Emotion (CFEE) dataset, we show that our model can first learn basic expressions and then progressively recognize compound expressions. Experiments demonstrate improved accuracy, stronger knowledge retention, and reduced forgetting. This framework contributes to the development of emotionally intelligent AI systems with applications in education, healthcare, and adaptive user interfaces.
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