博碩士論文 103525005 詳細資訊

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姓名 黃千鳳(Chien-Feng Huang)  查詢紙本館藏   畢業系所 軟體工程研究所
論文名稱 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)
★ 以元件式概念為基礎設計建立的XBRL示範性平台★ 利用指定功能軌跡的滑鼠特徵分析以提升識別率
★ 應用方位感測器之手機使用者識別機制★ 基於加速度及方位感測器之智慧型手機動態動作識別機制
★ 以組合專精型多分類器於財務危機預測之研究★ 結合領域知識與機器運算之新的特徵選取方法: 應用於財務危機預警預測之問題
★ 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★ 基因演算法運用於特徵挑選解決財務危機預測問題
★ 基於方位感測器與觸控螢幕之智慧型手機非侵入式多模組識別機制★ 非侵入式多模組之手機使用者識別機制 :基於動態方法
★ 多分類器組合應用於財務危機預測★ 漸進式模型應用於財務危機預測問題
★ Bus Arrival Prediction - to Ensure Users Not to Miss the Bus (Preliminary Study based on Bus Line 243 Taipei)★ 公車路線規劃系統之資料自動收集系統實作
★ KVM 高可用性群集設計與實作★ 特徵挑選方法和分類器在財務危機預測問題中比較
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摘要(中) 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。
摘要(英) 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.
關鍵字(中) ★ 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
關鍵字(英) ★ Non-intrusive Authentication Mechanism
★ Vehicle Security
★ Driver Verification
★ Gaussian Mixture Model
★ Support Vector Machine
★ Smartwatch
論文目次 中文摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vi
一、緒論 1
1-1 研究背景 1
1-2 研究動機 2
1-3 研究目的 4
1-4 論文架構 4
二、文獻探討 5
2-1 駕駛者行為模式生物特徵識別技術 5
2-2 個別型高斯混合模型之駕駛者識別 6
2-3 支持向量機 8
2-4 生物特徵驗證效能評估指標 9
三、系統架構 10
3-1 資料前處理 10
3-1-1 訊號切割 11
3-1-2 訊號正規化 11
3-1-3 中位數濾波器 11
3-1-4 動態資料 12
3-2 特徵轉換 13
3-2-1 個人高斯混合模型 14
3-2-2 距離公式 14
3-3 系統建模 15
3-4 駕駛者驗證 15
四、實驗與討論 16
4-1 駕駛模擬環境 16
4-2 資料收集 17
4-3 實驗:Bhattacharyya-based Distance作為建模特徵 18
4-4 實驗結果討論 20
4-4-1 動態資料 20
4-4-2 距離公式 22
五、結論 26
參考文獻 27
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指導教授 梁德容(Deron Liang) 審核日期 2016-8-29
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