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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/71926

    Title: 利用高斯混合模型及支持向量機之 駕駛者生物特徵驗證研究;Driver Verification based on Biometric using GMM and SVM
    Authors: 黃千鳳;Huang,Chien-Feng
    Contributors: 軟體工程研究所
    Keywords: 非侵入式識別機制;汽車安全;駕駛者識別;高斯混合模型;支持向量機;穿載式裝置;Non-intrusive Authentication Mechanism;Vehicle Security;Driver Verification;Gaussian Mixture Model;Support Vector Machine;Smartwatch
    Date: 2016-08-29
    Issue Date: 2016-10-13 14:06:46 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 汽車與日常生活密不可分,而車輛安全的問題卻也持續發生中,而隨著生物辨識技術的發展,駕駛者識別方法也愈來愈多樣,雖然有助於減少車輛安全問題,但還是有缺失或是未發展完全。
    ;Today, vehicles have been an essential part of our daily life. One-third of drivers admit they have left their vehicle while it is idling, which makes the vehicle an easy target of theft. In recent years, many verification methods had been developed, but there is still a room for better result.
    In this research, a novel method of driver verification by combining Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) is proposed. The proposed method is based on the hypothesis that drivers have their own specific driving behaviors; and the driving behaviors can be captured from smartwatch sensors and used as behavioral biometrics for driver recognition. In order to validate this hypothesis, a simulation system has been established to collect 50 drivers’ driving behavioral information, and the experimental result shows there are same methods to improve this experimental approach.
    Appears in Collections:[軟體工程研究所 ] 博碩士論文

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