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研究生:藍偉銘
研究生(外文):Wei-ming Lan
論文名稱:基於空間分佈模型與支援向量機之人臉辨識系統
論文名稱(外文):Face Recognition System Based on Spatial Constellation Model and Support Vector Machine
指導教授:洪西進洪西進引用關係
指導教授(外文):Si-jin Hong
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:資訊工程系
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:2010
畢業學年度:98
語文別:英文
論文頁數:76
中文關鍵詞:人臉辨識局部二元化圖形
外文關鍵詞:face recognitionLBPs
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本論文提出基於空間群集模型並使用進階多解析度區塊局部二元化圖形(Advanced Multi-resolution Block Local Binary Pattern)作為特徵點的人臉辨識系統,並以ORL face database以及Extended Yale B face database來進行實驗。
傳統上我們在做人臉辨識多半使用取特徵的方式是以所謂以模型(Model)為基礎的方式來對影像降維並擷取特徵,這類型方式對輸入影像資料的處理多半是所謂全域的(Holistic),例如:主成分分析(Principle Component Analysis)、線性識別分析(Linear Discriminant Analysis),會使用這類型的模型來對資料做模擬除了此類型方法早已廣在統計領域上被用於資料分類模擬外,也是因為這類型的方式算是會利用資料目前的相互特性來做模擬,是一種子空間學習的演算法(Sub-Space Learning Algorithm),可對於後續作分類有加強的效果,但由於此類全域模擬的方法,會造成辨識效果容易受到取像變異的影響,例如:光源或是影像正規化不足等等。所以近年來以往用於圖型識別的紋理描述器(Descriptor)漸漸受到關注應用於人臉辨識或是其他生物辨識上,例如:賈伯濾波器(Gabor Filter)、局部二元化圖形(Local Binary Pattern)。
會使用紋理描述器來取得我們需要的影像特徵原因是因為這類型的紋理描述器所取得影像的特徵資訊是以局部的方式取得(Local),這就代表著我們可以避開一些以全域模擬影像資料的缺點,例如:部分的影像資訊破壞(Local Occlusion)以及影像正規化不足的問題,且此類的描述器描述出來的資訊受光源的影響較不是那麼的敏感。本篇論文使用局部二元化圖形(Local Binary Pattern)為基礎,改進傳統局部二元化圖形的一些缺點,進而利用這些不同局部二元化圖形擷取出來特徵點在影像上分布的情形來做為我們辨識的依據。我們利用可以良好模擬自然界現象的高斯混合化模型(Gaussian Mixture Model)來模擬取得這些特徵點分布的資訊。運用高斯混合化模型取得的參數,我們建構出可以良好抵抗位移(Shift)、尺寸(Scale)、旋轉(Rotation)的特徵資訊,最後我們運用多類支持向量機(Multi-Class Support Vector Machines)做訓練產生分類器,配合投票的方式決定辨識的結果以及對非系統的人做過濾。
This research presents a comprehensive recognition system based on spatial constellation model using advanced multi-resolution block local binary patterns. We perform object-oriented design code to build our system and do the experiments of face recognition using ORL and Extended Yale B face database.
We usually do dimension reduction and get features from facial images based on model-based method for the conventional face recognition. This kind of methods is holistic when they deal with the image data, for example, PCA (Principle Component Analysis), LDA (Linear Discriminent Analysis) and so on. Researcher use this kind of methods is because they are good methods to do dimension reduction in statistics. They find the relation of input data which are good for we to represent the data. However, these holistic model methods are easily affected by the variance of images we get such like illumination change and insufficient normalization. In the past few years, some descriptors like Gabor Filter and local binary pattern used in pattern recognition are noticed and be used in face recognition or other biometrics. Because these descriptors get the features from images are local, that is why we use it. We can avoid insufficient normalization problem that usually happen in holistic method. Our research is based on LBP (local binary pattern) and we use Gaussian Mixture Model, it’s a good method to model the phenomena in the nature world, to model the distribution of the characteristic points in spatial domain. Using parameters of GMM we get, we can construct good features which can robust against the distortion of input image like variation of shift, scale, and rotation. We use multi-class
II
support vector machines to train our data in the database, and we make the final decision by counting the votes of each support vector machine.
ABSTRACT I
CONTENTS III
LIST OF FIGURES V
LIST OF TABELS VIII
CHAPTER1 INTRODUCTION 1
1.1 THE PURPOSE AND BACKGROUND OF RESEARCH 1
1.2 MOTIVATION OF RESEARCH 2
1.3 ARCHITECTURE OF PAPER 3
SECTION 2 FACE RECOGNITION 4
2.1 FACE DETECTION 4
2.2 FACE FEATURE EXTRACTION 5
2.3 CLASSIFICATION 20
SECTION 3 PROCESS OF RESEARCH AND ALGORITHM 28
3.1 ARCHITECTURE OF FACE RECOGNITION SYSTEM 28
3.2 RESEARCH PROCESS 29
3.2.1 Choose the Texture Descriptor 29
3.2.2 Problem of Local Binary Pattern and Improvement 29
3.2.3 Problems of Traditional LBP When We Do Recognition 36
3.2.4 Make Sure Uniqueness 38
3.2.5 Get the Information of Spatial Distribution Based on LBP 39
3.2.6 Gaussian Mixture Models[9][25] [26] 40
3.2.7 Utilize Parameters of GMM to Get Features Against to Shift, Scale, and Rotation 43
3.3 CLASSIFIER 46
CHAPTER4 SYSTEM ARCHITECTURE AND EXPERIMENT RESULT 50
4.1 SYSTEM ARCHITECTURE 50
4.2 FACE DETECTION AND FACE IMAGE NORMALIZATION [28] 51
4.2.1 Integral Image 51
4.2.3 Cascaded Classifiers 54
4.2.3 Normalization of Facial Image and Pre-processing 56
CHAPTER5 CONCLUSION 67
5.1 CONCLUSION 67
REFERENCE 73
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