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Face Recognition For Drunk Driver

This project's objective is to determine whether there is a discernible difference between the face behaviours of persons who are drunk and those of people who are sober. There is a significant amount of face recognition that occurs in situations involving intoxicated persons. It is imperati...

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Bibliographic Details
Main Author:	Muhammad Fakhrul Alif, Heirman
Format:	Final Year Project Report
Language:	English English
Published:	Universiti Malaysia Sarawak (UNIMAS) 2023
Subjects:	T Technology (General)
Online Access:	http://ir.unimas.my/id/eprint/42996/1/MUHAMMAD%20FAKHRUL%20ALIF%20BIN%20HEIRMAN%20%2824pgs%29.pdf http://ir.unimas.my/id/eprint/42996/2/MUHAMMAD%20FAKHRUL%20ALIF%20BIN%20HEIRMAN%20%28Fulltext%29.pdf http://ir.unimas.my/id/eprint/42996/
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id	my.unimas.ir.42996
record_format	eprints
spelling	my.unimas.ir.429962023-10-12T04:12:20Z http://ir.unimas.my/id/eprint/42996/ Face Recognition For Drunk Driver Muhammad Fakhrul Alif, Heirman T Technology (General) This project's objective is to determine whether there is a discernible difference between the face behaviours of persons who are drunk and those of people who are sober. There is a significant amount of face recognition that occurs in situations involving intoxicated persons. It is imperative that intoxicated drivers be identified as soon as possible, preferably before they get behind the wheel, to reduce the likelihood that an accident will take place. At the outset, a comparison is made between the facial behaviour of a drunk person and that of a sober person. This is followed by the collection of comprehensive data regarding the distinction between the two face behaviours through the utilisation of the data that is produced following the training of the digital images through Image Processing. This procedure has the potential to demonstrate that the distinction between intoxicated and sober people can be identified based on facial behaviour, and that the distinction can be spotted using computer vision. The first approach for achieving the project's objectives is to run the face detection method, which detects the red colour on the eyes, nose, and cheeks by utilising Haar Cascade to identify just the face and obtain the confidence rate value. The project proceeds by completing the training and testing processes for all digital photographs, which are classified into three cases. A total of 100 digital images of sober face and 100 digital images of drunk face from single individuals for case 1. For case 2, 150 digital images of drunk face and 150 digital images of sober face from multiple individuals. As for case 3, a total of 50 digital images of sober faces and 50 digital images of drunk faces from multiple individuals. To meet the project's goals, a probability comparison from the testing procedure between drunk and sober is performed. The confidence rate of red colour detection for eyes, nose and cheek is between 75% until 98% for drunk face and 35% until 74% for sober face. Meanwhile, the probability of drunk face is approximately between 0.94 until 0.99 and probability of sober face is between 0.62 until 0.84 for case 1. Next, the probability of drunk face is between 0.79 until 0.95 and the probability for sober face is between 0.78 until 1.00 for case 2. For case 3, the probability of drunk face is between 0.62 until 0.99 and the probability of between 0.91 until 0.99 was for sober face. Universiti Malaysia Sarawak (UNIMAS) 2023 Final Year Project Report NonPeerReviewed text en http://ir.unimas.my/id/eprint/42996/1/MUHAMMAD%20FAKHRUL%20ALIF%20BIN%20HEIRMAN%20%2824pgs%29.pdf text en http://ir.unimas.my/id/eprint/42996/2/MUHAMMAD%20FAKHRUL%20ALIF%20BIN%20HEIRMAN%20%28Fulltext%29.pdf Muhammad Fakhrul Alif, Heirman (2023) Face Recognition For Drunk Driver. [Final Year Project Report] (Unpublished)
institution	Universiti Malaysia Sarawak
building	Centre for Academic Information Services (CAIS)
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Sarawak
content_source	UNIMAS Institutional Repository
url_provider	http://ir.unimas.my/
language	English English
topic	T Technology (General)
spellingShingle	T Technology (General) Muhammad Fakhrul Alif, Heirman Face Recognition For Drunk Driver
description	This project's objective is to determine whether there is a discernible difference between the face behaviours of persons who are drunk and those of people who are sober. There is a significant amount of face recognition that occurs in situations involving intoxicated persons. It is imperative that intoxicated drivers be identified as soon as possible, preferably before they get behind the wheel, to reduce the likelihood that an accident will take place. At the outset, a comparison is made between the facial behaviour of a drunk person and that of a sober person. This is followed by the collection of comprehensive data regarding the distinction between the two face behaviours through the utilisation of the data that is produced following the training of the digital images through Image Processing. This procedure has the potential to demonstrate that the distinction between intoxicated and sober people can be identified based on facial behaviour, and that the distinction can be spotted using computer vision. The first approach for achieving the project's objectives is to run the face detection method, which detects the red colour on the eyes, nose, and cheeks by utilising Haar Cascade to identify just the face and obtain the confidence rate value. The project proceeds by completing the training and testing processes for all digital photographs, which are classified into three cases. A total of 100 digital images of sober face and 100 digital images of drunk face from single individuals for case 1. For case 2, 150 digital images of drunk face and 150 digital images of sober face from multiple individuals. As for case 3, a total of 50 digital images of sober faces and 50 digital images of drunk faces from multiple individuals. To meet the project's goals, a probability comparison from the testing procedure between drunk and sober is performed. The confidence rate of red colour detection for eyes, nose and cheek is between 75% until 98% for drunk face and 35% until 74% for sober face. Meanwhile, the probability of drunk face is approximately between 0.94 until 0.99 and probability of sober face is between 0.62 until 0.84 for case 1. Next, the probability of drunk face is between 0.79 until 0.95 and the probability for sober face is between 0.78 until 1.00 for case 2. For case 3, the probability of drunk face is between 0.62 until 0.99 and the probability of between 0.91 until 0.99 was for sober face.
format	Final Year Project Report
author	Muhammad Fakhrul Alif, Heirman
author_facet	Muhammad Fakhrul Alif, Heirman
author_sort	Muhammad Fakhrul Alif, Heirman
title	Face Recognition For Drunk Driver
title_short	Face Recognition For Drunk Driver
title_full	Face Recognition For Drunk Driver
title_fullStr	Face Recognition For Drunk Driver
title_full_unstemmed	Face Recognition For Drunk Driver
title_sort	face recognition for drunk driver
publisher	Universiti Malaysia Sarawak (UNIMAS)
publishDate	2023
url	http://ir.unimas.my/id/eprint/42996/1/MUHAMMAD%20FAKHRUL%20ALIF%20BIN%20HEIRMAN%20%2824pgs%29.pdf http://ir.unimas.my/id/eprint/42996/2/MUHAMMAD%20FAKHRUL%20ALIF%20BIN%20HEIRMAN%20%28Fulltext%29.pdf http://ir.unimas.my/id/eprint/42996/
_version_	1781710344088977408
score	13.211869

Face Recognition For Drunk Driver

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