Bio-Inspired Real-Time Facial Emotion Detection using Yolov11

Facial emotion detection is a pivotal technology in the domain of HCI(Human-Computer Interaction), enabling systems to understand human emotions through facial cursor expressions. This project introduces a novel FER system that integrates bio-inspired optimization techniques with the advanced YOLOv11 deep learning model to enhance detection accuracy and efficiency. The YOLOv11 architecture is employed for its real-time object detection capabilities, while bio-inspired algorithms optimize the model’s hyper parameters, ensuring robust performance across diverse datasets. The system is trained and evaluated on comprehensive facial emotion datasets, achieving high accuracy in classifying multiple emotional states. This fusion of bio-inspired optimization with state-of-the-art deep learning models presents a significant advancement in the field of facial emotion recognition, offering potential applications in domains like emotional computing, cyber security, and improving user experiences.