Real-time classification for autonomous drowsiness detection using eye aspect ratio

作者：

Highlights：

• Real-time, non-intrusive and low computational cost tool for drowsiness detection.

• Comparison of MLP, RF and SVM-based models.

• Feedback approach to customize model for each user.

• Inter- and intra-analysis of subjects from an external database (DROZY).

摘要

•Real-time, non-intrusive and low computational cost tool for drowsiness detection.•Comparison of MLP, RF and SVM-based models.•Feedback approach to customize model for each user.•Inter- and intra-analysis of subjects from an external database (DROZY).

论文关键词：Real-time drowsiness detection,Computer vision,Machine learning,Support vector machine,Eye aspect ratio,Human reliability

论文评审过程：Received 18 July 2019, Revised 6 April 2020, Accepted 1 May 2020, Available online 4 May 2020, Version of Record 20 May 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113505