Enhancing Facial Expression Recognition with LSTM through Dual-Direction Attention Mixed Feature Networks and CLIP

We present our contribution to the 8th ABAW challenge at CVPR 2025, where we tackle valence-arousal estimation, emotion recognition, and facial action unit detection as three independent challenges. Our approach leverages the well-known Dual-Direction Attention Mixed Feature Network (DDAMFN) for all three tasks, achieving results that surpass the proposed baselines. Additionally, we explore the use of CLIP for the emotion recognition challenge as an additional experiment. To enhance performance, we incorporate LSTM layers into our models, allowing them to capture temporal dependencies in facial expressions and emotional states. This integration proves particularly beneficial for valence-arousal prediction and action unit detection, leading to notable improvements. We provide insights into the architectural choices that contribute to the strong performance of our methods.