Driver Drowsiness Detection Using CNN and HAAR

Author(s)	Mr. Gaurav Singh, Vikas Yadav, Durgesh Gaur, Nancy Agrawal
Country	India
Abstract	A major risk factor for traffic accidents is tiredness among drivers . It is a serious issue that emphasizes the necessity of real- time, efficient detection and preventive techniques. In order to effectively identify driver drowsiness, this research presents a hybrid technique that combines Convolunal Neural Network(CNN) and Haar Cascade. Because of its effectiveness in real-time application, the Haar Cascade method is employed for the detection of facial and eye regions. A CNN is then used to examine the identified eye regions and determine if the driver is awake orsleepy. High accuracy in identifying patterns of drowsiness is ensured by training the machine on annotated datasets of eye pictures.The suggested method strikes a balance between accuracy and speed, which qualifies it for real-time vehicle time vehicle deployment. The durability and dependability of this approach are demonstrated by experimental fundings, offering a workable way to improve road safety.
Keywords	Deep learning algorithm(CNN and HAAR Cascade)
Field	Computer Applications
Published In	Volume 7, Issue 2, March-April 2025
Published On	2025-03-24
DOI	https://doi.org/10.36948/ijfmr.2025.v07i02.39716
Short DOI	https://doi.org/g89v6g

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research