1. Support Vector Machine for Behavior-Based Driver Identification System
- Author
-
Xiaoning Meng, Xinyu Wu, Yongsheng Ou, Huihuan Qian, and Yangsheng Xu
- Subjects
Password ,Article Subject ,General Computer Science ,Computer science ,business.industry ,lcsh:Mechanical engineering and machinery ,Fast Fourier transform ,Human behavior ,Machine learning ,computer.software_genre ,Independent component analysis ,Support vector machine ,Control and Systems Engineering ,Principal component analysis ,Feature (machine learning) ,lcsh:TJ1-1570 ,Data pre-processing ,Artificial intelligence ,business ,computer - Abstract
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.
- Published
- 2010