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...
| Main Authors: | , , , |
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| Format: | Book Chapter |
| Language: | English |
| Published: |
Springer US
2007
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/38176/1/Driver_Recognition_System_Using_FNN_and_Statistical_Methods.pdf |
| _version_ | 1796878992340418560 |
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| author | Abdul Rahman, Abdul Wahab Tan, Chin Keong Abut, Huseyin Takeda, Kazuya |
| author_facet | Abdul Rahman, Abdul Wahab Tan, Chin Keong Abut, Huseyin Takeda, Kazuya |
| author_sort | Abdul Rahman, Abdul Wahab |
| collection | IIUM |
| 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. |
| first_indexed | 2024-03-05T23:29:15Z |
| format | Book Chapter |
| id | oai:generic.eprints.org:38176 |
| institution | International Islamic University Malaysia |
| language | English |
| last_indexed | 2024-03-05T23:29:15Z |
| publishDate | 2007 |
| publisher | Springer US |
| record_format | dspace |
| spelling | oai:generic.eprints.org:381762020-06-04T07:37:27Z http://irep.iium.edu.my/38176/ Driver recognition system using FNN and statistical methods Abdul Rahman, Abdul Wahab Tan, Chin Keong Abut, Huseyin 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, Huseyin 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 |
| spellingShingle | HA Statistics Abdul Rahman, Abdul Wahab Tan, Chin Keong Abut, Huseyin Takeda, Kazuya Driver recognition system using FNN and statistical methods |
| title | 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_short | Driver recognition system using FNN and statistical methods |
| title_sort | driver recognition system using fnn and statistical methods |
| topic | HA Statistics |
| url | http://irep.iium.edu.my/38176/1/Driver_Recognition_System_Using_FNN_and_Statistical_Methods.pdf |
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