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

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姓名

楊哲豪(Che Hao-Yang) 查詢紙本館藏

畢業系所

軟體工程研究所

論文名稱

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)

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摘要(中)

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。
本論文提出一種新的防止汽車失竊的非侵入式驗證機制，透過直線路段及轉彎路段的實驗資料，每隔一段時間進行一次驗證，並在比較所有實驗結果後推薦最佳組合，而此最佳組合的EER效果可以達到9.5%。
本論文的EER效果可以達到9.5%，表示此非侵入式驗證機制可以解決現今人們因為覺得不方便而不使用的防盜系統問題，而且在未來此非侵入式驗證機制可以與其他驗證機制合併來更加降低失竊率，以增加汽車的安全性。
關鍵字:汽車安全、非侵入式使用者識別

摘要(英)

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.

關鍵字(中)

★ 汽車安全
★ 非侵入式使用者識別

關鍵字(英)

★ Vehicle Security
★ Non-Intrusive User Authentication

論文目次

中文摘要 i
Abstract ii
致謝 iii
一、緒論 1
1.1. 研究背景 1
1.2. 研究動機 4
1.3. 研究目的 5
1.4. 論文架構 6
二、文獻探討 7
2-1.驗證方法探討 7
2-2.重新驗證機制(Re-Authentication)[3] 7
2-3.研究方法探討 8
2-4收集資料探討 10
三、實驗設計 12
3-1.資料收集 12
3-1-1.資料收集環境 12
3-1-2影像資料轉換 16
3-1-3骨架節點三維座標資料轉換 16
3-2.資料前處理 18
3-2-1.區段切割 18
3-2-2.特徵角度轉換 18
3-2-3.識別特徵轉換及正規化 20
3-3.系統建模 22
3-3-1.抽樣方式 22
3-3-2.分類器 24
3-3-3.效能評估標準 27
3-3-4.模型訓練與測試 29
3-3-5.實驗一介紹 29
3-3-6.實驗二介紹 31
四、實驗結果與分析 35
4-1.實驗一結果及分析 35
4-2.實驗二結果及結果分析 38
五、結論與未來展望 46
5.1. 結論 46
5.2. 未來展望 47
參考文獻 48

參考文獻

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[3] Pusara and C. E. Brodley, "User re-authentication via mouse movements," in Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security, 2004, pp. 1-8.
[4] L. O’Gorman,“Comparing passwords, tokens, and biometrics for user authentication,”Proc. IEEE, vol. 91, no. 12, pp. 2019–2040, Dec. 2003.
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[14] City Car Driving. Car Simulator. Available from: http://citycardriving.com.
[15] Logitech Inc. G27 Racing Wheel. Available from: http://en.wikipedia.org/wiki/Logitech_G27 .
[16] iPi Soft. iPi Motion Capture™ Software. Available from: http://ipisoft.com.
[17] C. Cortes and V. Vapnik, "Support-vector networks," Machine learning, vol. 20, pp. 273-297, 1995.
[18] F.Lin, D.Liang, and E.Chen,“Financial ratio selection for businesscrisis prediction,”Expert Systems with Applications,vol.38, pp.15094-15102, 2011.
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指導教授

梁德容(Deron-Liang)

審核日期

2014-10-13

推文