Back to all available

Research on adaptive driver drowsiness detection method based on multi-facial information of drivers

human-computer-interaction
artificial-intelligence
computer-vision
data-mining-and-machine-learning
neural-networks

Abstract

Driver drowsiness detection is an important application in the field of intelligent driving. In this paper, to solve the problem of driver drowsiness detection based on facial expression feature recognition, an Adaptive Driver Detection Model (ADDM) is proposed. ADDM adopts a dual-driven approach based on data and prior knowledge to integrate multi-source facial information, capturing the coordinated dynamics among different facial regions and overcoming the high misjudgment rate caused by using only one or more facial action units (such as mouth and/or eye regions). Additionally, ADDM combines class information (K-means), temporal information, and attention information to address the poor generalization caused by individual differences among drivers. Finally, ADDM employs a Graph Convolutional Network (GCN) to model the relationships among facial regions, enhancing detection performance by allowing information exchange between nodes. Experiments conducted on two public benchmark datasets demonstrate that the proposed ADDM method outperforms the state-of-the-art (SOTA) methods and shows excellent performance in drowsiness detection.
Ask to review this manuscript

Notes for potential reviewers

  • Volunteering is not a guarantee that you will be asked to review. There are many reasons: reviewers must be qualified, there should be no conflicts of interest, a minimum of two reviewers have already accepted an invitation, etc.
  • This is NOT OPEN peer review. The review is single-blind, and all recommendations are sent privately to the Academic Editor handling the manuscript. All reviews are published and reviewers can choose to sign their reviews.
  • What happens after volunteering? It may be a few days before you receive an invitation to review with further instructions. You will need to accept the invitation to then become an official referee for the manuscript. If you do not receive an invitation it is for one of many possible reasons as noted above.
  • PeerJ Computer Science does not judge submissions based on subjective measures such as novelty, impact or degree of advance. Effectively, reviewers are asked to comment on whether or not the submission is scientifically and technically sound and therefore deserves to join the scientific literature. Our Peer Review criteria can be found on the "Editorial Criteria" page - reviewers are specifically asked to comment on 3 broad areas: "Basic Reporting", "Experimental Design" and "Validity of the Findings".
  • Reviewers are expected to comment in a timely, professional, and constructive manner.
  • Until the article is published, reviewers must regard all information relating to the submission as strictly confidential.
  • When submitting a review, reviewers are given the option to "sign" their review (i.e. to associate their name with their comments). Otherwise, all review comments remain anonymous.
  • All reviews of published articles are published. This includes manuscript files, peer review comments, author rebuttals and revised materials.
  • Each time a decision is made by the Academic Editor, each reviewer will receive a copy of the Decision Letter (which will include the comments of all reviewers).

If you have any questions about submitting your review, please email us at [email protected].