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研究生:黎玉線
研究生(外文):Ngoc Tuyen Le
論文名稱:自適應奇異值分解之影像增強應用於人臉識別和指紋分類
論文名稱(外文):Image Enhancement Using Adaptive Singular Value Decomposition for Face Recognition and Fingerprint Classification
指導教授:王敬文
指導教授(外文):Jing-Wein Wang
口試委員:洪冠明李俊宏王敬文陳文淵李建樹王周珍
口試委員(外文):Kuan-Ming HungChung-Hong LeeJing-Wein WangWen-Yuan ChenJiann-Shu LeeChou-Chen Wang
口試日期:2015-05-21
學位類別:博士
校院名稱:國立高雄應用科技大學
系所名稱:電子工程系碩士班
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:2015
畢業學年度:103
語文別:英文
論文頁數:111
中文關鍵詞:二維離散傅立葉轉換二維離散小波轉換人臉辨識指紋分類影像增強亮度補償奇異值分解
外文關鍵詞:2D discrete Fourier transforms2D discrete wavelet transformface recognitionfingerprint classificationimage enhancementillumination compensationsingular value decomposition
相關次數:
  • 被引用被引用:9
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人臉辨識和指紋分類技術的研發,在現今科技發展上仍然是研究人員的重大挑戰,而影響人臉辨識和指紋分類系統最關鍵的因素則為輸入影像的品質。為了改善影像前處理步驟中的影像品質,本論文採用奇異值分解的特性來改善彩色人臉影像及灰階指紋影像的品質。
在人臉辨識這項研究部份,我們提出三個方法用以增進彩色人臉影像品質。第一個方法,在空間域使用兩個各自獨立參考影像的雙層奇異值分解亮度補償演算法;第二個方法,有效的亮度檢測器先用於亮度偵測,再使用自適應奇異值分解於二維離散傅立葉轉換亮度補償演算法;第三個方法,亮度檢測器同樣先用於亮度偵測,再使用自適應奇異值分解於二維離散小波轉換亮度補償演算法。當光線不足時,上述方法都可以有效解決在亮度變化時所引起的彩色人臉影像變異問題,同時可提高辨識系統的辨識率。
指紋影像增強是自動指紋辨識系統中最重要的步驟之一,在這項研究中,我們提出一個有效的演算法來增強指紋影像的品質。該演算法係由兩階段組成:第一階段,透過二維離散小波轉換,將輸入指紋影像分解成四個子頻帶;在第二階段,藉由參考用的高斯模板影像及四個子頻帶自適應地求出補償係數,再對指紋影像進行補償,由實驗結果得以呈現本方法的優越性能。
The development of face recognition and fingerprint classification systems in real world still remains a major challenge for the scientific researchers. One of the most crucial reasons affecting the efficiency of these systems is the quality of the input image. To improve the quality of images in pre-processing step, this dissertation applies the useful properties of the singular value decomposition in image processing to improve quality of the color face and fingerprint images.
For face recognition, this study proposes three methods to enhance color face images. First, we propose the innovative illumination compensation algorithm, two separated singular value decomposition, in the spatial domain. Second, we introduce an efficient brightness detector for lighting detection and an illumination compensation method, adaptive singular value decomposition in the two-dimensional discrete Fourier domain. Third, we propose a novel illumination compensation method called adaptive singular value decomposition in the 2D discrete wavelet domain. These methods can resolve the illumination variation problem on color face images when there is insufficient light and, at the same time, improve the effective of recognition system.
Fingerprint image enhancement is one of the most major steps in an automated fingerprint identification system. In this study, an effective algorithm for fingerprint image quality improvement is proposed. The algorithm consists of two stages. The first stage is decomposing the input fingerprint image into four subbands by applying two-dimensional discrete wavelet transform. At the second stage, the compensated image is produced by adaptively obtaining the compensation coefficient for each subband based on the image content and the referred Gaussian template. The experimental results indicated the efficiency of the proposed method.
Abstract i
Acknowledgments iii
List of figures vii
List of tables x
Chapter 1 Organization 1
1.1 Motivation 1
1.2 Contributions 2
1.3 Dissertation Organization 3
Chapter 2 Introduction 4
2.1 Face Image Enhancement Methods for Recognition 4
2.2 Fingerprint Image Enhancement Methods for Classification 7
2.3 Singular Value Decomposition in Digital Image Processing 9
2.3.1 Singular Value Decomposition of a Color Face Image 9
2.3.2 Singular Value Decomposition of a Fingerprint Image 10
2.4 Fourier Transform in Digital Image Processing 11
2.5 Wavelet Transform in Digital Image Processing 13
2.6 The Databases Use for Color Face Recognition 16
2.6.1 The CMU-PIE Face Database 16
2.6.2 The Color FERET Database 16
2.6.3 The FEI Face Database 17
2.7 The Databases Use for Fingerprint Classification 17
2.7.1 The NIST-4 Fingerprint Database 18
2.7.2 The FVC 2002 Fingerprint Database. 18
Chapter 3 Color Face Image Enhancement Using SVD in the Spatial Domain for Face Recognition 19
3.1 Introduction 19
3.2 Illumination Compensation Based on Two- Separated Singular Value Decomposition 20
3.3 Experimental Results and Discussion 27
3.3.1 Projection Color Space Variation 27
3.3.2 Results Obtained Using the CMU-PIE Color Face Database 28
3.3.3 Results Obtained Using the Color FERET Face Database 32
3.4 Summary 34
Chapter 4 Color Face Image Enhancement Using SVD in Fourier Domain for Face Recognition 35
4.1 Introduction 35
4.2 Illumination Compensation for Face Recognition by Using Adaptive SVD in the Fourier Domain 35
4.2.1 Illumination Compensation by Global ASVDF 35
4.2.2 Illumination Compensation by Region-Based ASVDF 40
4.2.3 Illumination Compensation by LASVDF Based on Pixel Localization Process 41
4.2.3 Illumination Compensation by ASVDF 42
4.3 Experimental Results and Discussion 47
4.3.1 Strategy for Gaussian Template Selection 48
4.3.2 Results for CMU-PIE Face Database 49
4.3.3 Results for Color FERET Database 53
4.3.4 Results for FEI Face Database 55
4.3.5 Computational Complexity 57
4.4 Summary 57
Chapter 5 Color Face Image Enhancement Using SVD in Wavelet Domain for Face Recognition 58
5.1 Introduction 58
5.2 Illumination Compensation for Face Recognition by Using Adaptive SVD in the Wavelet Domain 58
5.2.1 Lighting detection 58
5.2.2 Illumination Compensation by Using ASVDW 59
5.2.3 Illumination Compensation by Using the Region-based ASVDW 64
5.3 Experimental Results and Discussion 65
5.3.1 Strategy for the Gaussian Template 65
5.3.2 Results of the CMU-PIE Face Database 66
5.3.3 Results of the Color FERET Database 72
5.3.4 Results of the FEI Face Database 73
5.3.5 Computational Complexity 75
5.4 Summary 75
Chapter 6 Fingerprint Image Enhancement Using SVD in Wavelet Domain for Classification 76
6.1 Introduction 76
6.2 Fingerprint Image Enhancement 76
6.3 Experimental Results and Discussion 79
6.4 Summary 84
Chapter 7 Conclusions and Future Researches 85
7.1 Conclusions 85
7.2 Future Researches 86
References 87
Abbreviations 92
Biography 93
Publications List 94
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