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研究生:羅方鈞
研究生(外文):Fang-Chun Lo
論文名稱:基於模糊類神經網路之人臉辨識
論文名稱(外文):Fuzzy Neural Network Based Face Recognition
指導教授:吳俊德吳俊德引用關係
指導教授(外文):Gin-De Wu
口試委員:洪志偉莊家峰
口試日期:2011-07-06
學位類別:碩士
校院名稱:國立暨南國際大學
系所名稱:電機工程學系
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:33
中文關鍵詞:模糊類神經網路人臉辨識尺度不變特徵轉換
外文關鍵詞:Fuzzy Neural NetworkFace RecognitionScale Invariant Feature Transform
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近年來影像處理與機械視覺技術不斷進步,人臉辨識也成為一個重要技術。因此本論文提出一套使用模糊類神經網路(FNN)分類器來進行人臉辨識。本論文是採用局部特徵當作人臉特徵參數。本文將人臉辨識分為兩大部分。第一部分利用尺度不變特徵轉換(SIFT)的技術,從人臉圖片中找出特徵值。尺度不變特徵值轉換分為空間尺度極值偵測(Scale-space extrema detection)、關鍵點定位(Keypoint localization)、定向分配(Orientation assignment)、關鍵點描述(Keypoint descriptor)四個階段產生影像特徵值集合。第二部分,將所有人臉的特徵值當作模糊類神經網路(FNN)輸入值做為最後的判斷,為了快速訓練我們使用了二元樹分類法,建構出二元樹。實驗結果顯示本文所提出的方法,將可以提高人臉辨識度。
In recent years, image processing and robot vision technology continues to development, and the human face recognition has become an important technology; therefore, this thesis presents a human face recognition system based on fuzzy neural network. The local feature is used as the facial feature parameters.The face recognition method which is presented consists of two steps. First, using the Scale Invariant Feature Transform (SIFT) method to find the features from the pictures. The Scale Invariant Feature Transform (SIFT) method has four parts, Scale-space extrema detection, Keypoint localization, Orientation assignment, Keypoint descriptor. In the second step, we take all features as FNN inputs to make the final decision. In order to training fast that we use the method of binary tree, and build a binary tree. The experiment results display that the method can identification human face efficiently.
Contents

Acknowledgement .................i
Chinese abstract ........ii
English abstract .......iii
Contents ................iv
List of tables .................v
List of figures ................vi


Chapter 1 Introduction ....1
1.1 Motivation 1
1.2 Related works ....3
1.3 Research process ....5
1.4 Organization of this thesis.....5
Chapter 2 Maximizing-Discriminability-Based Self-Organizing Fuzzy Networ 6
2.1 Introduction of MDFNN ...6
2.2 Structure of MDFNN ...........7
2.3 Learning algorithm of MDFNN ........10
Chapter 3 Scale Invariant Feature Transform 13
3.1 Scale-space extrema detection ........14
3.2 Accurate Key point Localization ........17
3.3 Orientation assignment ................19
3.4 The local image descriptor ................20
Chapter 4 Experimental results ................22
Chapter 5 Conclusions ........................29

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