Abstract: This research proposes a comprehensive system designed to detect stress levels among IT professionals by leveraging real-time facial analysis powered by advanced machine learning techniques. The underlying concept is based on the psychological understanding that human emotions are visually expressed through subtle facial movements and micro-expressions, making them valuable indicators for assessing an individual’s mental and emotional state. By utilizing these non-intrusive cues, the system offers a privacy-conscious and continuous approach to psychological monitoring without requiring active user participation. At the core of the system is the use of Convolutional Neural Networks (CNNs), which are highly effective in processing and interpreting visual data, particularly for emotion recognition tasks. The Deep Face library is employed to extract deep feature representations from facial images, enabling accurate classification of emotions that correlate with varying levels of psychological stress. For initial face detection and localization, classical Haar Cascade classifiers are integrated, providing reliable identification of facial regions within both static images and live video streams. The implementation includes a web-based interface developed using the Django framework, which allows users to interact with the system in real time. This interface supports continuous webcam input, emotion-based feedback display, and optional logging of stress assessments, ensuring a user-friendly experience suitable for deployment in organizational settings. Experimental evaluations were conducted using both publicly available emotion datasets and live webcam feeds to validate the system's effectiveness. The results indicate high accuracy and consistent performance in classifying emotional states and estimating corresponding stress levels. These findings underscore the system's potential as a practical tool for real-time mental health monitoring in professional environments, particularly within the high-pressure context of the IT industry.
Keywords: Stress Identification, Facial Emotion Analysis, Machine Learning, Deep Face, CNN, Real Time Monitoring, IT Workforce.
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DOI:
10.17148/IJARCCE.2025.14639
