Enhancing Facial Expression Recognition through Dual-Direction Attention Mixed Feature Networks: Application to 7th ABAW Challenge
This is an incremental improvement for facial expression analysis researchers/engineers, applying an existing architecture to a specific challenge.
The authors tackled multitask facial expression recognition (valence-arousal, emotion, action units) using a Dual-Direction Attention Mixed Feature Network, achieving results far beyond the baseline in the 7th ABAW challenge.
We present our contribution to the 7th ABAW challenge at ECCV 2024, by utilizing a Dual-Direction Attention Mixed Feature Network (DDAMFN) for multitask facial expression recognition, we achieve results far beyond the proposed baseline for the Multi-Task ABAW challenge. Our proposal uses the well-known DDAMFN architecture as base to effectively predict valence-arousal, emotion recognition, and facial action units. We demonstrate the architecture ability to handle these tasks simultaneously, providing insights into its architecture and the rationale behind its design. Additionally, we compare our results for a multitask solution with independent single-task performance.