Real-Time Stress Detection and Analysis using Facial Emotion Recognition

Abstract:  " Real-Time Stress Detection and Analysis using Facial Emotion Recognition" is an innovative system designed for real-time stress detection and analysis through facial emotion recognition. Leveraging the power of machine learning and computer vision techniques, the system can accurately identify and analyze emotions exhibited by individuals in live video streams. By utilizing a pre-trained deep learning model, the system detects facial expressions associated with various stress levels, including "Bursted," "Irritated," "Anxious," "Relaxed," "Neutral," "Broked," and "Shocked." The project integrates with a web application interface where users can visualize comprehensive stress analysis reports generated from the detected emotions over time. Through detailed graphs and charts, users can explore trends such as emotion distribution over time, average stress levels, and daily stress variations. Additionally, the system provides personalized recommendations based on the user's emotional patterns, aiming to improve overall well-being.

Keywords:  Real-Time, Stress Detection, Analysis, Facial Emotion Recognition Cite: Hari Prasad Chandika, Bulla Soumya, Baireddy Naveen Eswar Reddy, Boda Mohana Sri Sai Manideep,"Real-Time Stress Detection and Analysis using Facial Emotion Recognition", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13324.