Abstract: Stress detection using facial expressions has become an important area of research in the field of human-computer interaction and mental health monitoring. This paper proposes a novel approach for identifying stress levels based on facial expression analysis. Stress is a common psychological condition that can negatively affect an individual's health and performance. Traditional methods of stress detection are often intrusive or rely on self-reporting, which can be inaccurate. By leveraging facial expression recognition techniques, this study aims to provide a non-invasive, real-time solution for assessing stress levels. The system utilizes machine learning algorithms to analyze facial features such as eye movement, brow furrowing, and mouth position to classify stress intensity. A dataset of labeled facial expressions corresponding to different stress levels was used to train the model. The results demonstrate the potential of using facial expression analysis as a reliable method for stress detection, with promising applications in healthcare, education, and workplace settings. Future work will focus on improving accuracy, real-time processing capabilities, and integration with other physiological indicators of stress.
Keywords: Stress Detection, Facial Expression Recognition, Emotion Recognition, Affective Computing Facial Feature Analysis ,Stress Classification, Emotion Classification , Mental Health Monitoring.
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