Support Vector Machine for Behavior-Based Driver Identification System
We present an intelligent driver identification system to handle vehicle theft based on modeling dynamic human behaviors. We propose to recognize illegitimate drivers through their driving behaviors. Since human driving behaviors belong to a dynamic biometrical feature which is complex and difficult...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2010-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2010/397865 |
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| Summary: | We present an intelligent driver
identification system to handle vehicle theft based on modeling
dynamic human behaviors. We propose to recognize illegitimate
drivers through their driving behaviors. Since human driving
behaviors belong to a dynamic biometrical feature which is
complex and difficult to imitate compared with static features
such as passwords and fingerprints, we find that this novel
idea of utilizing human dynamic features for enhanced security
application is more effective. In this paper, we first describe
our experimental platform for collecting and modeling human
driving behaviors. Then we compare fast Fourier transform
(FFT), principal component analysis (PCA), and independent
component analysis (ICA) for data preprocessing. Using machine
learning method of support vector machine (SVM), we derive the individual
driving behavior model and we then demonstrate
the procedure for recognizing different drivers by analyzing
the corresponding models. The experimental results of learning
algorithms and evaluation are described. |
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| ISSN: | 1687-9600 1687-9619 |