Improving Emotion Aware Facial Recognation with Integrated Feature Statergies

In today’s fast-moving world, mental health and emotional well-being are becoming more important than ever. Recognizing human emotions and stress levels using facial images has become a growing area of research. Facial expressions carry useful information about a person’s emotional state and can be captured through image processing and artificial intelligence. This research focuses on developing a smart system that can detect human emotions like happiness, sadness, anger, fear and also stress levels by analyzing facial images. We used advanced machine learning and deep learning techniques, including CNN, SVM, KNN, to identify facial features and classify different emotional states. For stress detection, we explored subtle changes in facial muscle movements, eye rejoins, and forehead tension, lip movement, which are not easily visible to the human eye but can be detected using image based models. The system is trained on standard JAFEE’s dataset and is further validated on primary dataset images to ensure accuracy in real world conditions. Our aim is to support mental health professionals, educational institutions, and workplaces by providing a non-invasive and automated tool for understanding emotional and stress conditions. These features help in identifying emotions such as happiness, sadness, anger, fear and surprise. Once the emotion is detected, the system checks if the stress is positive or negative. The results show promising accuracy and can be extended for mobile health application, emotion aware virtual assistants, and human computer interaction systems.