Drunkenness detection using a CNN with adding Gaussian noise and blur in the thermal infrared images Online publication date: Thu, 27-Oct-2022
by Kha Tu Huynh; Huynh Phuong Thanh Nguyen
International Journal of Intelligent Information and Database Systems (IJIIDS), Vol. 15, No. 4, 2022
Abstract: Drunkenness is now often regarded as one of society's most serious issues. The majority of road accidents are caused by drunk driving. This paper proposes a methodology based on evaluating a facial thermal infrared image, adding noise and filters for augmentation, and determining intoxication using machine learning algorithms. In drunkenness detection, most research focus on using RGB image of facial expressions like eye sate, head position, or functional state indicators. Sometimes it is not trusty when attempting to predict on certain people who have certain facial feature patterns that the machine learning algorithm learned to be a factor of drunkenness. The combination of using the thermal infrared image with some noise and filter then predicting by optimised convolution neural network (CNN) model approach 93% on accuracy proves the efficiency as well as the feasibility of the proposed method.
Online publication date: Thu, 27-Oct-2022
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Information and Database Systems (IJIIDS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com