Feature Aggregation for Efficient Continual Learning of Complex Facial Expressions
This work addresses the need for emotionally intelligent AI systems that can adapt to dynamic human emotions without forgetting prior knowledge, with applications in education, healthcare, and adaptive user interfaces, but it is incremental as it builds on existing continual learning methods.
The paper tackles the problem of catastrophic forgetting in facial expression recognition for continual learning by proposing a hybrid framework that integrates deep convolutional features and facial Action Units, achieving improved accuracy and reduced forgetting on the CFEE dataset.
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.