Depression Analysis Using Face Recognition System

Stress is a part of life it is an unpleasant state of emotional arousal that people experience in situations like working for long hours in front of a computer. Computers have become a way of life, much life is spent on computers and hence we are therefore more affected by the ups and downs that they cause us. One cannot just completely avoid their work on computers but one can at least control his/her usage when being alarmed about him being stressed at a certain point of time. Monitoring the emotional status of a person who is working in front of a computer for a longer duration is crucial for the safety of a person. In this work, a real-time non-intrusive videos are captured, which detects the emotional status of a person by analyzing the facial expression. In each frame of the video, we identify a distinct emotion and assess stress levels over consecutive hours of captured footage. Our approach involves employing a methodology enabling model training and analysis of predictive variances. Utilizing Theano, a Python framework, we enhance both execution and development times for the linear regression model, serving as the deep learning algorithm in this context. Experimental findings indicate that our system effectively performs across diverse age groups with a generic model.