A Two-Stage Dual-Modality Model for Facial Emotional Expression Recognition

This project is an academic submission for the 10th Affective Behavior Analysis in-the-wild (ABAW) workshop. While it addresses complex real-world issues like motion blur and pose variation, it lacks commercial defensibility. Competition-specific models are typically highly specialized for a single dataset and lack the infrastructure to become a standalone product. With 0 stars and 2 forks, there is no evidence of community adoption or ecosystem growth. Furthermore, frontier labs (OpenAI, Google) are rapidly integrating native multimodal emotional intelligence into foundation models (e.g., GPT-4o's real-time emotional audio/visual capabilities), which renders specialized '8-class' emotion classifiers obsolete. Large cloud providers like AWS and Azure already offer mature 'Face' and 'Emotion' APIs, creating high platform domination risk. The project's primary value is as a reference implementation for researchers participating in the same competition series.