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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/66111

    Title: 一種新的非侵入式識別機制使用駕駛者的上半身骨架角度:基於動態及直方圖方法;A Non-Intrusive Authentication Mechanism Based on Dynamics and Histogram of Driver's Upper Body Joint Angles
    Authors: 楊哲豪;Che,Hao-Yang
    Contributors: 軟體工程研究所
    Keywords: 汽車安全;非侵入式使用者識別;Vehicle Security;Non-Intrusive User Authentication
    Date: 2014-10-13
    Issue Date: 2014-11-24 15:43:44 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 在現今的社會中,汽車在日常生活中已經成為我們不可或缺的一部分,雖然在市面上已存在許多汽車防盜設備,如方向盤鎖、指紋辨識系統等,但這些防盜設備都各有他們的缺點,導致在社會上還有許多汽車因而失竊,因此本論文針對汽車安全方面來進行研究。
    本論文使用的實驗資料為12位駕駛者,透過駕駛一條城鎮道路而收集到的駕駛行為資訊,最後得到的資料為每位駕駛者各25筆資料,本論文使用生物特徵識別技術的駕駛行為來做識別,我們認為在正常的情形下,每個駕駛在開車時的身體行為會有一種固定的模式,因此本實驗透過兩架Kinect攝影機錄製使用者的駕駛行為,並透過IPi Motion Capture System商業軟體將使用者的身體骨架節點轉為行為資訊,並搭配分類器進而判別是否為合法使用者。
    ;Nowadays, car has become an indispensable part in our daily life. However, the increasing car-stolen cases bring an unignorable issue to the society. To suppress those criminal cases, lots of invented anti-theft devices such as wheel locks, alarm system and fingerprint identification systems are widely used in the world. Since some weakness still exist in these devices, the number of criminal cases are kept in a high level. Therefore, our study aims on the improvement of the vehicle security.
    The dataset in our experiment is collected from twelve participants including eleven males and one female, and each has 25 samples of behavior information recorded when they drive on the urban road. The biometric identification technology is applied to our new authentication mechanism which assumes every person has his/her own fixed patterns when they are driving. Also, the Kinect camera is used to film the behavior of participants. The transformed information is then processed for the driver identification by using KNN and linear SVM, which then can automatically recognize the genuine user or the imposter.
    More, a new authentication mechanism is proposed to raise the identification efficiency by additional verification from straight and curve road driving data. After tests of various combinations, we present a best combination which can reach 9.5% of EER. The result shows our new mechanism is promising in reinforcement for the anti-theft mechanism. In the future, the possible combination with other biometric mechanisms can be expected to reduce theft rate and also upgrade the vehicle protection.
    Appears in Collections:[軟體工程研究所 ] 博碩士論文

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