Leveraging Eye Movement for Instructing Robust Video-Based Facial Expression Recognition

Video-based facial expression recognition (VFER) is challenging due to variations caused by cultural background and expression camouflage. To tackle these problems, researchers introduced eye movement signals to complement visual information. However, existing methods either require expensive devices to capture high-quality eye movements or can only extract low-quality eye movements visually, making them ineffective in the real world. To address this, we propose an eye movement-instructed VFER (EM-VFER) that leverages high-quality eye movements to instruct the visual learning, obtaining robust performance without requiring costly devices during inference. Specifically, our EM-VFER operates in two stages: the high-quality eye movement pre-training stage and the eye movement-instructed video fine-tuning stage. In the pre-training, we compile an Eye-behavior-aided Multimodal Emotion Recognition (EMER) dataset and use it to train a multimodal Transformer. During the fine-tuning, we propose a novel progressive eye movement-instructed learning to take better advantage of the prior knowledge about high-quality eye movement signals from EMER. The instructed fine-tuning model could then make more robust predictions on downstream facial expression datasets. We evaluate our approach on three macroexpression datasets (DFEW, MAFW and Aff-wild2) and two micro-expression datasets (CASME III and CASME II). The results demonstrate that EM-VFER significantly outperforms existing methods. The code will be available.