PROJECT ABSTRACT

This article introduces the utilization of deep learning for real-time detection of human emotions. By employing a deep convolutional neural network, the study focuses on accurately identifying six universal emotions: happiness, sadness, fear, anger, disgust, and surprise. Leveraging a dataset comprising unique double eye images, the system considers surrounding features to capture human expressions effectively. This technology holds promise across various fields, from aiding individuals in distress by detecting fear to signalling potential risks through anger detection. Furthermore, recognizing emotions like sadness can facilitate support for those dealing with depression. By integrating these insights into intelligent systems, this approach aims to contribute to proactive crime prevention, mental health assessments, and interventions, fostering a safer, healthier, and more empathetic community.

PROJECT DOCUMENTATION

**The Team**