Driver Recognition Using Gaussian Mixture Models and Decision Fusion Techniques

Authors:
Kristin S. Benli;Remzi Düzagaç;M. Taner Eskil
Affiliations:
Department of Computer Engineering, Işık University, Şile, Istanbul, Turkey;Department of Computer Engineering, Işık University, Şile, Istanbul, Turkey;Department of Computer Engineering, Işık University, Şile, Istanbul, Turkey
Venue:
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Year:
2008

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Abstract

In this paper we present our research in driver recognition. The goal of this study is to investigate the performance of different classifier fusion techniques in a driver recognition scenario. We are using solely driving behavior signals such as break and accelerator pedal pressure, engine RPM, vehicle speed, steering wheel angle for identifying the driver identities. We modeled each driver using Gaussian Mixture Models, obtained posterior probabilities of identities and combined these scores using different fixed and trainable (adaptive) fusion methods. We observed error rates as low as 0.35% in recognition of 100 drivers using trainable combiners. We conclude that the fusion of multi-modal classifier results is very successful in biometric recognition of a person in a car setting.