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Driver Recognition Based on Dynamic Handgrip Pattern on Steeling Wheel

Authors: Ronghua Chen; Mary Fenghua She; Xiangping Sun; Lingxue Kong; Yuchuan Wu;

Driver Recognition Based on Dynamic Handgrip Pattern on Steeling Wheel

Abstract

This paper proposes a novel biometric authentication method based on the recognition of drivers' dynamic handgrip on steering wheel. A pressure sensitive mat mounted on a steering wheel is employed to collect handgrip data exerted by the hands of drivers who intend to start the vehicle. Then, the likelihood-ratio-based classifier is designed to distinguish rightful driver of a car after analyzing their inherent dynamic features of grasping. The experimental results obtained in this study show that mean acceptance rates of 85.4% for the trained subjects and mean rejection rates of 82.65% for the un-trained ones are achieved by the classifier in the two batches of testing. It can be concluded that the driver verification approach based on dynamic handgrip recognition on steering wheel is a promising biometric technology and will be further explored in the near future in smart car design.

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    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Top 10%
Top 10%
Average
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