: Stress is the body's natural reaction to external and internal stimuli. Despite being something natural, prolonged exposure to stressors
can contribute to serious health problems. These reactions are reflected not only physiologically, but also psychologically, translating into
emotions and facial expressions. Based on this, we developed a proof of concept for a stress detector. With a convolutional neural network. We
also designed a deep neural network that receives facial landmarks as input to take advantage of the fact that eye moments, lips moment, and
head movements are different from normal situations when a person is stressed. These results show that the proposed algorithm recognizes
stress more effectively. We are using convolutional neural network (CNN) for classification and training purpose capable of classifying facial
expressions, and an application that uses this model to classify real-time images of the user's face and thereby assess the presence of signs of
stress. The results obtained are very promising and the proposed stress- detection system is noninvasive, only requiring a webcam to monitor the
user facial expressions.
Keywords: Face recognition, Stress detection, Convolutional Neural Network(CNN)