STRESS DETECTION IN IT PROFESSIONAL USING FACE RECOGNITION (FOREHEAD IMAGE)

Abstract: The increasing workload and pressure in the IT industry often lead to elevated stress levels among professionals, affecting their health and productivity. This research proposes a novel approach to detect stress in IT professionals using face recognition technology focusing on the forehead region. By analyzing images and incorporating additional inputs like body temperature, oxygen levels, and sleep hours, we employ logistic regression Artificial intelligence algorithms to predict stress levels. The proposed system aims to offer an efficient and non-intrusive method for early stress detection, facilitating timely interventions to improve employee well-being. The methodology involves preprocessing the images to enhance feature extraction, followed by the application of convolutional neural networks (CNN) to identify stress-related patterns. The system is trained to recognize these patterns and classify the stress levels accurately. To validate the effectiveness of the approach, the model's performance is evaluated using metrics such as accuracy, precision, recall, and F1 score, and compared with existing stress detection methods.

Keywords: Artificial intelligence, logistic regression, CNN, ROI algorithms.