Image Based Stress Detection Using Deep Learning

Stress is a prevalent and detrimental aspect of modern life, impacting mental and physical well-being. The ability to detect and manage stress is of paramount importance. This study proposes a novel approach for stress detection through image analysis using Convolutional Neural Networks (CNN), particularly the efficient Mobile Net architecture. The core idea is to leverage facial expressions as indicators of stress levels, as they often exhibit distinctive patterns during stressful situations. An extensive dataset of facial images encompassing various stress levels is collected and preprocessed. Mobile Net, known for its efficiency and effectiveness in image classification tasks, is employed as the backbone architecture for feature extraction. The CNN model is fine-tuned using transfer learning techniques to adapt to the stress detection task. During training, the network learns to recognize subtle facial cues associated with stress, allowing it to classify input images into different stress levels. The proposed system demonstrates promising results in real-time stress detection, offering a non-intrusive and accessible means for assessing stress in individuals. This innovative application of deep learning and Mobile Net architecture has the potential to provide valuable insights into stress management and mental health monitoring, ultimately improving the well-being of individuals in our fast-paced, stress-prone society.