Driver recognition using gaussian mixture models and decision fusion techniques

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Tarih

2008

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Yayıncı

Springer-Verlag Berlin

Erişim Hakkı

info:eu-repo/semantics/closedAccess

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Özet

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 mid trainable (adaptive) fusion methods. We observed error rates is 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.

Açıklama

Anahtar Kelimeler

Recognition, Vehicle, Gaussian Mixture Model, Decision Fusion, Biometrics, Blind source separation, Classifiers, Communication channels (information theory), Image segmentation, Learning systems, Mixtures, Object recognition, Trellis codes, Accelerator pedals, Classifier fusions, Decision fusion, Driving behaviors, Error Rate (ER), Fusion methods, Gaussian mixture model, Gaussian mixture models (GMMs), Posterior probabilities, Recognition, Vehicle, Vehicle speeds, Automobile drivers

Kaynak

Lecture Notes in Computer Science

WoS Q Değeri

Q4

Scopus Q Değeri

Q3

Cilt

5370

Sayı

Künye

Benli, K. S., Düzağaç, R. & Eskil, M. T. (2008). Driver recognition using gaussian mixture models and decision fusion techniques. Lecture Notes in Computer Science, 5370, 803-811. doi:10.1007/978-3-540-92137-0_88