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The Journal of The Korea Institute of Intelligent Transport Systems Vol.9 No.3 pp.67-72
생체기반 GMM Supervector Kernel을 이용한 운전자검증 기술
Driver Verification System Using Biometrical GMM Supervector Kernel
Abstract
This paper presents biometrical driver verification system in car experiment through analysis of speech, and face information. We have used Mel-scale Frequency Cesptral Coefficients (MFCCs) for speaker verification using speech information. For face verification, face region is detected by AdaBoost algorithm and dimension-reduced feature vector is extracted by using principal component analysis only from face region. In this paper, we apply the extracted speech- and face feature vectors to an SVM kernel with Gaussian Mixture Models(GMM) supervector. The experimental results of the proposed approach show a clear improvement compared to a simple GMM or SVM approach.
