Estimating blood alcohol level through facial features for driver impairment assessment
Document Type
Conference Proceeding
Publication Title
Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
First Page
4527
Last Page
4536
Publisher
IEEE
School
School of Science
RAS ID
70349
Funders
Raine Medical Research Foundation
Abstract
Drunk driving-related road accidents contribute significantly to the global burden of road injuries. Addressing alcohol-related harm, particularly during safety-critical activities like driving, requires real-time monitoring of an individual's blood alcohol concentration (BAC). We devise an in-vehicle machine learning system that harnesses standard commercial RGB cameras to predict critical levels of BAC. Our system can detect instances of alcohol intoxication impairment as subtle as 0.05 g/dL (WHO recommended legal limit for driving), with an accuracy of 75%, by leveraging the physiological manifestations of alcohol intoxication on a driver's face. This system holds great promise for improving road safety. In tandem, we have compiled a data set of 60 subjects engaged in simulated driving scenarios, spanning three levels of alcohol intoxication. These scenarios were captured and divided into video segments labeled "sober","low', and "severe"Alcohol Intoxication Impairment (AII), constituting the basis for evaluating our system's performance. To the best of our knowledge, this study is the first to create a large-scale real-life dataset of alcohol intoxication and assess intoxication levels using an off-the-shelf RGB camera to detect drunk driving.
DOI
10.1109/WACV57701.2024.00448
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Comments
Keshtkaran, E., von Berg, B., Regan, G., Suter, D., & Gilani, S. Z. (2024). Estimating blood alcohol level through facial features for driver impairment assessment. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 4539-4548). https://doi.org/10.1109/WACV57701.2024.00448