An Ensemble Method for Fatigue Detection based on Multiple Facial Expressions

Abstract

People who spend a lot of time staring at screens need to be aware of their own level of fatigue because it can lower productivity and increase the risk of accidents. In this research work, we suggest a novel method for detecting fatigue based on a variety of facial expressions. We collected 12,185 facial images of people using digital screens, which contains 6108 normal face images and 6077 stress face images. In this framework, we used different multiple facial expressions techniques and Artificial Neural Network (ANN) to label neutral faces as normal, while sad and disgusted faces were labeled as stressed. We used this dataset to train a deep learning model to classify facial images as normal or stressful. Our model achieved highest accuracy rates of 88% for testing and showed good performance on a trained dataset. We also validated the accuracy of our approach by comparing the predicted stress/non-stress labels with ground truth labels obtained through self-report. The results of this study demonstrate the potential of using multiple facial expressions for fatigue detection in people who are using digital screens. This approach could be used in the future to develop real-time fatigue detection systems that could alert people when they are showing signs of stress or fatigue and help them take a break or adjust their work schedule.