Paper Contents
Abstract
Stress is a significant concern in todays fast-paced world, impacting both mental and physical health. Early detection of stress is essential for effective management and intervention. This research introduces a novel image-based stress detection model utilizing Convolutional Neural Networks (CNN), particularly the MobileNet architecture. Facial expressions serve as primary indicators of stress levels, captured and analyzed using deep learning techniques. The study involves the collection and preprocessing of a diverse dataset of facial images, training a MobileNet-based CNN model through transfer learning to recognize stress-related facial features. The proposed system offers a non-invasive, real-time approach to stress monitoring, contributing to mental health research and workplace well-being initiatives.
Copyright
Copyright © 2025 Nithish Kumar Pamidi . This is an open access article distributed under the Creative Commons Attribution License.
