Driver recognition system using FNN and statistical methods

Advancements in biometrics-based authentication have led to its increasing prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely currently on electronic alarm or smart card systems. A biometric driver recognition system utilizing driving behavior signals...

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Bibliographic Details
Main Authors:	Abdul Rahman, Abdul Wahab, Tan , Chin Keong, Abut, Hus, Takeda, Kazuya
Format:	Book Chapter
Language:	English
Published:	Springer US 2007
Subjects:	HA Statistics
Online Access:	http://irep.iium.edu.my/38176/ http://irep.iium.edu.my/38176/ http://irep.iium.edu.my/38176/ http://irep.iium.edu.my/38176/1/Driver_Recognition_System_Using_FNN_and_Statistical_Methods.pdf

id	iium-38176
recordtype	eprints
spelling	iium-381762014-09-11T08:22:16Z http://irep.iium.edu.my/38176/ Driver recognition system using FNN and statistical methods Abdul Rahman, Abdul Wahab Tan , Chin Keong Abut, Hus Takeda, Kazuya HA Statistics Advancements in biometrics-based authentication have led to its increasing prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely currently on electronic alarm or smart card systems. A biometric driver recognition system utilizing driving behavior signals can be incorporated into existing vehicle security system to form a multimodal identification system and offer a higher degree of protection. The system can be subsequently integrated into intelligent vehicle systems where it can be used for detection of any abnormal driver behavior with the purposes of improved safety or comfort level. In this chapter, we present features extracted using Gaussian Mixture Models (GMM) from accelerator and brake pedal pressure signals, which are then employed as input to the driver recognition module. A novel Evolving Fuzzy Neural Network (EFuNN) was used to illustrate the validity of the proposed system. Results obtained from the experiments are compared with those of statistical methods. They show potential of the proposed recognition system to be used in real-time scenarios. A high identification rate and the low verification error rate were indicated considerable difference in the way different drivers apply pressure to the pedals. Springer US 2007 Book Chapter PeerReviewed application/pdf en http://irep.iium.edu.my/38176/1/Driver_Recognition_System_Using_FNN_and_Statistical_Methods.pdf Abdul Rahman, Abdul Wahab and Tan , Chin Keong and Abut, Hus and Takeda, Kazuya (2007) Driver recognition system using FNN and statistical methods. In: Advances for in-vehicle and mobile systems. Challenges for International Standards . Springer US, Spring Street, USA, pp. 11-23. ISBN 978-0-387-33503-2 (P), 978-0-387-45976-9 (O) http://link.springer.com/chapter/10.1007/978-0-387-45976-9_2 10.1007/978-0-387-45976-9_2
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	HA Statistics
spellingShingle	HA Statistics Abdul Rahman, Abdul Wahab Tan , Chin Keong Abut, Hus Takeda, Kazuya Driver recognition system using FNN and statistical methods
description	Advancements in biometrics-based authentication have led to its increasing prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely currently on electronic alarm or smart card systems. A biometric driver recognition system utilizing driving behavior signals can be incorporated into existing vehicle security system to form a multimodal identification system and offer a higher degree of protection. The system can be subsequently integrated into intelligent vehicle systems where it can be used for detection of any abnormal driver behavior with the purposes of improved safety or comfort level. In this chapter, we present features extracted using Gaussian Mixture Models (GMM) from accelerator and brake pedal pressure signals, which are then employed as input to the driver recognition module. A novel Evolving Fuzzy Neural Network (EFuNN) was used to illustrate the validity of the proposed system. Results obtained from the experiments are compared with those of statistical methods. They show potential of the proposed recognition system to be used in real-time scenarios. A high identification rate and the low verification error rate were indicated considerable difference in the way different drivers apply pressure to the pedals.
format	Book Chapter
author	Abdul Rahman, Abdul Wahab Tan , Chin Keong Abut, Hus Takeda, Kazuya
author_facet	Abdul Rahman, Abdul Wahab Tan , Chin Keong Abut, Hus Takeda, Kazuya
author_sort	Abdul Rahman, Abdul Wahab
title	Driver recognition system using FNN and statistical methods
title_short	Driver recognition system using FNN and statistical methods
title_full	Driver recognition system using FNN and statistical methods
title_fullStr	Driver recognition system using FNN and statistical methods
title_full_unstemmed	Driver recognition system using FNN and statistical methods
title_sort	driver recognition system using fnn and statistical methods
publisher	Springer US
publishDate	2007
url	http://irep.iium.edu.my/38176/ http://irep.iium.edu.my/38176/ http://irep.iium.edu.my/38176/ http://irep.iium.edu.my/38176/1/Driver_Recognition_System_Using_FNN_and_Statistical_Methods.pdf
first_indexed	2023-09-18T20:54:48Z
last_indexed	2023-09-18T20:54:48Z
_version_	1777410232934203392

Driver recognition system using FNN and statistical methods

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