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研究生:林佩蓉
研究生(外文):Lin, Pei-Rong
論文名稱:基於最小相關LBPH獨立成分影像特徵 之瞌睡辨識系統
論文名稱(外文):Drowsiness Recognition using ICA Feature Vectors Based on Least Correlated LBPH
指導教授:連振昌連振昌引用關係
指導教授(外文):Lien, Cheng-Chang
學位類別:碩士
校院名稱:中華大學
系所名稱:資訊工程學系碩士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:英文
論文頁數:44
中文關鍵詞:瞌睡辨識眼睛狀態眨眼局部二值化圖樣統計圖(LBPH)獨立成分分析支持向量機
外文關鍵詞:drowsiness recognitioneye stateLBPHICAsupport vector machine
相關次數:
  • 被引用被引用:0
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  • 下載下載:48
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近年來,瞌睡辨識研究廣泛應用於駕駛瞌睡偵測、與遠距教學系統中,而其中眼睛狀態辨識更是瞌睡辨識研究裡穩固的基石。然而,基於視覺影像方法裡,眼睛狀態的特徵是瞌睡辨識中不可或缺的一環,並可概括分為兩大類:一為基於樣板方法、二為基於特徵方法。本論文提出新穎的瞌睡辨識方法解決了現行方法易產生的問題,此方法包含獨創的特徵,最小相關LBPH,接著將此特徵使用獨立成分分析方法獲得低維度的統計獨立性特徵向量,最後將上述的新特徵向量使用支持向量機訓練出一個眼睛狀態分類器。在瞌睡辨識研究中,根據前人所研究的眨眼生理反應,我們設計出四個法則配合前述的眼睛狀態分類器即建構完成瞌睡辨識系統。本瞌睡辨識系統可在兩秒內判定瞌睡與否,辨識正確率98%、每張畫面的眼睛辨識時間需0.08秒。
In recent years, the drowsiness detection is widely applied to the driver alerting or distance learning. The drowsiness recognition system is constructed on the basis of the recognition of eye states. In this thesis, we propose a new image feature called “least correlated LBP histogram (LC-LBPH)” to generate a high discriminate image features for eye states recognition. Then, the method of independent component analysis (ICA) is used to derive the low-dimensional and statistical independent feature vectors. Finally, support vector machines (SVM) is trained to recognize the drowsiness. Furthermore, we design four rules to recognize three eye transition patterns which define the normal (consciousness), drowsiness, and sleeping situations. Experimental results show that our system can recognize the drowsiness with accuracy 98% within 2 seconds. The eye-state recognition rate is about 0.08 seconds per frame.
摘要 i
Abstract ii
致謝 iii
Contents iv
List of Table v
List of Figure vi
Chapter 1 Introduction 1
Chapter 2 Least Correlated LBPH Feature 4
2.1 Face and Eyes Detection 4
2.2 Least Correlated LBPH 5
2.3 Pattern Weighting 10
Chapter 3 Independent Component Analysis (ICA) of LC-LBPH Feature 14
3.1 Introduction of ICA 14
3.2 Reduction of Feature Dimension 14
Chapter 4 Drowsiness Recognition 21
4.1 Support Vector Machine for Eye state Recognition 21
4.2 Drowsiness Recognition System 25
Chapter 5 Experimental Results 28
5.1 Training Data and Test Data 28
5.2 Eye State and Drowsiness Recognition 28
Chapter 6 Conclusion 33
Reference 34

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