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研究生:謝秉澂
研究生(外文):Ping-Cheng Hsieh
論文名稱:線性投影法於人臉辨識之研究
論文名稱(外文):A Study on Linear Projection Methods for Face Recognition
指導教授:董必正董必正引用關係
指導教授(外文):Pi-Cheng Tung
學位類別:博士
校院名稱:國立中央大學
系所名稱:機械工程研究所
學門:工程學門
學類:機械工程學類
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:61
中文關鍵詞:獨立成份分析法主成份分析法光線補償人臉辨識特徵擷取
外文關鍵詞:Independent component analysisPrincipal component analysisShadow compensationFeature extractionFace recognition
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由國際生物辨識組織的研究報告得知,利用人類生理特徵作為識別個人身分的生物辨識技術,將成為未來安全機制的主流。而在各種生物辨識方法中,人臉辨識在使用上最為簡便與人性化,若將其結合視訊監控系統,更可主動對範圍內的行人進行身分的比對與確認,也因此人臉辨識成為目前最熱門的研究主題之一。

在本文中,研究的重心是集中於以人臉外表特徵(appearance feature)為基礎之特徵擷取法上,其中最具代表性的方法是主成份分析法與獨立成份分析法。由於傳統的計算架構通常將整張人臉影像視為單一樣式(pattern),而人臉局部特徵的統計資訊則有被忽略的可能,故後來有學者提出以次樣式(sub-pattern)為基礎的計算架構,試圖改善傳統方法之缺點。然而,我們進行一系列的實驗後發現,在某些情況下,次樣式特徵擷取架構的辨識率反而低於傳統方式。針對此一現象,本文進行深入的分析與探討,並提出合理的物理解釋。最後,為了同時考慮人臉整體與局部特徵的統計資訊,本文發展出一混合型特徵擷取架構,經由三個人臉資料庫測試的結果顯示,此架構有更佳的辨識性能。

此外,許多研究文獻指出:同一人的影像因受到光線變化所造成的改變,通常大於不同人影像間的差異,因此,光線變異對辨識性能的影響可見一斑。為了改善這個問題,本文提出一個光線補償方法,其作法是:先對影像中的局部區域進行直方圖等化,以強化局部特徵,接著透過鏡射與改良式影像平均的技術,融合左右兩邊的人臉資訊,以達到光線補償的效果。此方法具有以下特點:(a)架構簡單,容易實現;(b)能強化人臉主要特徵,並將非人臉主要特徵標準化;(c)能直接應用於單張影像,而無須預知光源方向,或使用多張影像進行訓練。
In a variety of biometric techniques, face recognition is a natural, non-intrusive, and user-friendly scheme. Especially, face recognition has the great advantage of being able to in places with large concourse of unaware visitors. After the Sept. 11, 2001 terrorist attacks, this active property of recognizing identity is not only garnering much more attention in both academia and industry, but also pushes face recognition turn into one of the most popular research topics.

In this dissertation, we mainly focus our attention on the investigation of appearance- based face recognition methods. Since the conventional appearance-based methods (e.g. Eigenfaces and Fisherfaces techniques) usually consider whole face image as a single pattern, some local statistical information of face image may be ignored. In order to emphasize the local facial features, some researches proposed their approaches which try to find the local appearance features by sub-pattern technique. Nevertheless, after a serious of experiments, we found that in some case the recognition accuracy of the sub-pattern based method is lower than that of the corresponding conventional method. In the former half of this dissertation, we presented our opinions to reasonably explain what causes this result. Then, by simultaneously considering global and local information of face images, we developed a novel hybrid approach for face recognition. The experimental results show that the proposed hybrid approach has better recognition performance than that obtained using other traditional methods.

In addition, some literatures pointed out that the intra-person variations caused by illumination change are often larger than the inter-person differences. Consequently, illumination variation is key factor that can significantly affect system face recognition performances. In the latter half of this dissertation, we proposed a novel shadow compensation method for dealing with this problem. Among others, this method has several advantages: (1) it is very simple so it is easily implemented in a real-time face recognition system; (2) it is able to reinforce key facial features and to standardize other parts of the face; (3) it can apply directly to single face image without any prior information of light source direction.
Abstract i
中文摘要 iii
誌謝 iv
Table of Contents v
List of Figures vii
List of Tables ix
Explanation of Symbols x

Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Survey of the Related Works 3
1.3 Dissertation Organization 7

Chapter 2 Overview of Two Basic Linear Projection Methods 9
2.1 Principal Component Analysis (PCA) 9
2.1.1 Calculating Eigenfaces 10
2.1.2 Using Eigenfaces to Classify a Face Image 10
2.2 Two-dimensional Principal Component Analysis (2DPCA) 11

Chapter 3 A Novel Hybrid Approach Based on Sub-pattern Technique and Whitened PCA for Face Recognition 13
3.1 Outline of This Chapter 13
3.2 Proposed Approaches 14
3.2.1 Image Partition for Constructing Sub-pattern Images 14
3.2.2 Sub-pattern Based PCA I (Sp-PCA I) 14
3.2.3 Sub-pattern Based PCA II (Sp-PCA II) 16
3.3 Experimental Results and Analysis 17
3.3.1 Yale Database - Expression Variant Test and Leave Out One Test 18
3.3.2 Yale B Database – Lighting Variant Test 19
3.3.3 Weizmann Database – Expression and Lighting Variant Test 20
3.3.4 Discussion 24
3.3.5 A Novel Hybrid Approach Combines PCA II and Sp-PCA I 24
3.4 Summary 25

Chapter 4 A Shadow Compensation Method Based on Facial Symmetry and Image Average for Face Recognition 27
4.1 Introduction 27
4.2 Proposed Method 30
4.3 Experiments and Results 35
4.3.1 Experiments on Yale B Face Database 35
4.3.2 Experiments on Weizmann Face Database 40
4.4 Discussion 41
4.4.1 The Mirror Operation’s Advantage and Limitation 41
4.4.2 The R-HEIA’s Robustness and Practicality 44
4.4.3 Comparisons with Other Well-known Methods 49

Chapter 5 Conclusions and Future Directions 50
5.1 Conclusions 50
5.2 Future Directions 51

References 52
Publications 58
Appendix – Mathematical Background of PCA 59
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