Driving in a state of drowsiness is a major cause of road accidents, resulting in tremendous damage to life and property. Developing robust, automatic, real-time systems that can infer drowsiness states of drivers has the potential of making life-saving impact. However, developing drowsiness detection systems that work well in real-world scenarios is challenging because of the difficulties associated with collecting high-volume realistic drowsy data and modeling the complex temporal dynamics of evolving drowsy states. In this paper, we propose a data collection protocol that involves outfitting vehicles of overnight shift workers with camera kits that record their faces while driving. We develop a drowsiness annotation guideline to enable humans to label the collected videos into 4 levels of drowsiness: `alert', `slightly drowsy', `moderately drowsy' and `extremely drowsy'. We experiment with different convolutional and temporal neural network architectures to predict drowsiness states from pose, expression and emotion-based representation of the input video of the driver's face. Our best performing model achieves a macro ROC-AUC of 0.78, compared to 0.72 for a baseline model.
@article{arxiv.2010.11162,
title = {In-the-wild Drowsiness Detection from Facial Expressions},
author = {Ajjen Joshi and Survi Kyal and Sandipan Banerjee and Taniya Mishra},
journal= {arXiv preprint arXiv:2010.11162},
year = {2020}
}
Comments
Paper from HSIM Workshop at IEEE Intelligent Vehicles Symposium 2020 (IV2020)