Driver recognition using gaussian mixture models and decision fusion techniques
Yükleniyor...
Tarih
2008
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer-Verlag Berlin
Erişim Hakkı
info:eu-repo/semantics/closedAccess
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