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-AVC of 0.78, compared to 0.72 for a baseline model.
In-the-wild Drowsiness Detection from Facial Expressions
2020-10-19
4035925 byte
Conference paper
Electronic Resource
English
IN-THE-WILD DROWSINESS DETECTION FROM FACIAL EXPRESSIONS
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