Driver identification and driver's emotion verification using KDE and MLP neural networks

A Driver's behavior is a major factor that contributes to the high accidents rates. However, if we are able to identify their behavior, it may be possible for us to detect driving idiosyncrasies that may prevent accidents. Therefore, this paper presents some simple and effective methods for an...

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Bibliographic Details
Main Authors:	Md Nor, Norzaliza, Abdul Rahman, Abdul Wahab
Format:	Conference or Workshop Item
Language:	English
Published:	2010
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://irep.iium.edu.my/20830/ http://irep.iium.edu.my/20830/ http://irep.iium.edu.my/20830/1/Driver_identification_and_driver%27s_emotion_verification_using_KDE_and_MLP_neural_network.pdf

id	iium-20830
recordtype	eprints
spelling	iium-208302012-02-21T11:45:29Z http://irep.iium.edu.my/20830/ Driver identification and driver's emotion verification using KDE and MLP neural networks Md Nor, Norzaliza Abdul Rahman, Abdul Wahab QA75 Electronic computers. Computer science A Driver's behavior is a major factor that contributes to the high accidents rates. However, if we are able to identify their behavior, it may be possible for us to detect driving idiosyncrasies that may prevent accidents. Therefore, this paper presents some simple and effective methods for an in-car data acquisition in collecting real time driving data. The data has been classified into three different driver's condition which leads into accident. They are happy expression, talking on the phone and normal driving. These data will be used to investigate the effectiveness of a driver's behavior which focusing on the driver's response towards the brake and gas pedals as well as its rate of change. From these data we will demonstrate simple yet effective technique in driver identification and driver verification. We use the kernel density estimation (KDE) as tools to extract features. Then, we use these features to recognize the emotion of the driver by using multi layer perceptron (MLP) as classifiers. The enhancement of driver's security, safety and comfort driving can be derived trough the performances of the driver's emotion verification which contribute to the development in the area of intelligent vehicle driver verification system. 2010 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/20830/1/Driver_identification_and_driver%27s_emotion_verification_using_KDE_and_MLP_neural_network.pdf Md Nor, Norzaliza and Abdul Rahman, Abdul Wahab (2010) Driver identification and driver's emotion verification using KDE and MLP neural networks. In: 2010 International Conference on Information and Communication Technology for the Muslim World (ICT4M), 13-14 December 2010, Jakarta, Indonesia. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5971922&tag=1
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Md Nor, Norzaliza Abdul Rahman, Abdul Wahab Driver identification and driver's emotion verification using KDE and MLP neural networks
description	A Driver's behavior is a major factor that contributes to the high accidents rates. However, if we are able to identify their behavior, it may be possible for us to detect driving idiosyncrasies that may prevent accidents. Therefore, this paper presents some simple and effective methods for an in-car data acquisition in collecting real time driving data. The data has been classified into three different driver's condition which leads into accident. They are happy expression, talking on the phone and normal driving. These data will be used to investigate the effectiveness of a driver's behavior which focusing on the driver's response towards the brake and gas pedals as well as its rate of change. From these data we will demonstrate simple yet effective technique in driver identification and driver verification. We use the kernel density estimation (KDE) as tools to extract features. Then, we use these features to recognize the emotion of the driver by using multi layer perceptron (MLP) as classifiers. The enhancement of driver's security, safety and comfort driving can be derived trough the performances of the driver's emotion verification which contribute to the development in the area of intelligent vehicle driver verification system.
format	Conference or Workshop Item
author	Md Nor, Norzaliza Abdul Rahman, Abdul Wahab
author_facet	Md Nor, Norzaliza Abdul Rahman, Abdul Wahab
author_sort	Md Nor, Norzaliza
title	Driver identification and driver's emotion verification using KDE and MLP neural networks
title_short	Driver identification and driver's emotion verification using KDE and MLP neural networks
title_full	Driver identification and driver's emotion verification using KDE and MLP neural networks
title_fullStr	Driver identification and driver's emotion verification using KDE and MLP neural networks
title_full_unstemmed	Driver identification and driver's emotion verification using KDE and MLP neural networks
title_sort	driver identification and driver's emotion verification using kde and mlp neural networks
publishDate	2010
url	http://irep.iium.edu.my/20830/ http://irep.iium.edu.my/20830/ http://irep.iium.edu.my/20830/1/Driver_identification_and_driver%27s_emotion_verification_using_KDE_and_MLP_neural_network.pdf
first_indexed	2023-09-18T20:31:29Z
last_indexed	2023-09-18T20:31:29Z
_version_	1777408765543317504

Driver identification and driver's emotion verification using KDE and MLP neural networks

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