Scalable Context-Based Facial Emotion Recognition Using Facial Landmarks and Attention Mechanism

Deciphering emotions from a person's perspective is critical for meaningful human relationships. Enabling computers to interpret emotional cues similarly could significantly improve human-machine interaction. Accurate emotion recognition involves more than just analyzing facial expressions; it requires situational context and facial landmarks, which together reveal a broader range of emotional states. Existing emotion recognition frameworks primarily focus on facial imaging, often overlooking the contextual elements and the subtle significance of facial landmarks. This paper proposes a scalable approach to emotion recognition that combines situational context comprehension, accurate facial landmark detection, and facial feature analysis. Due to its scalability, our model can be applied across diverse computational platforms and operational circumstances while maintaining high performance. The model's robustness and utility were validated against the EMOTIC benchmark, achieving an impressive overall accuracy of 84%. The findings underscore the importance of incorporating contextual information and facial landmarks to enhance emotion recognition accuracy. This advancement is expected to contribute substantially to fields such as augmented reality, medical imaging, and sophisticated human-computer interaction systems.