臺灣博碩士論文加值系統

近年來由於生物特徵識別系統的需求日增，使得人臉辨識之研究更受重視。雖然指紋、掌紋或虹膜掃瞄等生物特徵也常被應用於身份識別或驗證之依據，但由於人臉辨識屬於非接觸式的機制，因此可以預期的是以人臉作為身份識別基礎的工具將會被廣泛的應用。
在取得紋理特徵上，多解析度區塊局部二元化圖樣(Multi-Scale Block Local Binary Pattern)相較於傳統區域性二元化圖樣，是以子區域(sub-region)的平均值取代個別的像素值(individual pixel)來做計算以取得較大結構(macrostructure)的紋理資訊，因此可以達到抵抗影像雜訊的效果，也保留局部二元化圖樣的優點。支持向量機(Support Vector Machine)是具有高效能的分類器之一，可對大量的資料作處理，因此也常被用在做人臉辨識的分類器。
根據多解析度區塊局部二元化圖樣之特徵擷取方法，本論文提出了一個基於局部紋理特徵空間分布與支持向量機之人臉辨識系統，其中採用一高斯混合化模型(Gaussian Mixture Model)來取得這些特徵點分布的資訊，以建構出一組可以良好抵抗位移、尺寸變化、旋轉的特徵資訊。然後再利用支持向量機來做訓練產生分類器，配合投票的方式作為最後辨識結果的依據。
在實驗中，本論文以ORL人臉資料庫來實驗測試本論文所提出的方法，並且針對此人臉資料庫影像分別做位移、尺寸變化、旋轉的處理，實驗結果證實本論文所提出之方法對於這些被破壞的影像確實具有很好的辨識能力與效果。

Recently, the research of face recognition is more important in biometric authentication field. Though the biometric characteristic of finger print, palm, iris are commonly using in ID authentication, face recognition is non-contact mechanism. We expect that face recognition would be applied to identification extensively.
In Multi-Scale Block Local Binary Pattern (MB-LBP), the computation is done based on average values of block sub-regions, instead of individual pixels for extracting the texture feature. MB-LBP not only preserves the advantage of Local Binary pattern (LBP) but also encodes macrostructures of image patterns. Support Vector Machine(SVM) is one of the efficient classifiers and be applied to the classifier of face recognition popularly.
From the base of MB-LBP feature extraction. we propose a face recognition system based on spatial distribution of local texture feature and support vector machine. We extract the spatial distribution of feature from Gaussian Mixture Model(GMM). Using the parameters of GMM we get, we can construct good features which can robust against the distortion of face image like shift, resize, and rotation.
In the experiment on using ORL facial databases, the proposed system did against the image variation (shifting, resize and rotation).

摘要 I
ABSTRACT...............................................III
誌謝.....................................................V
目錄....................................................VI
圖目錄................................................VIII
表目錄...................................................X
第一章 緒論..............................................1
第二章 相關研究探討......................................6
2.1 人臉偵測............................................6
2.2 主成分分析(PRINCIPLE COMPONENT ANALYSIS, PCA)......12
2.3 線性鑑別分析(LINEAR DISCRIMINANT ANALYSIS, LDA)....12
2.4 局部二元化圖樣.....................................15
2.5 高斯混合模型.......................................20
2.6 支持向量機(SUPPORT VECTOR MACHINES，SVM)...........24
2.6.1 線性可分離......................................27
2.6.2 線性不可分離....................................29
2.6.3 非線性可分離....................................30
第三章 一個基於局部紋理特徵空間分佈與支持像量機之
人臉辨識系統....................................36
3.1 人臉辨識系統架構...................................36
3.2 人臉偵測與人臉影像正規化.......................38
3.2.1 人臉偵測........................................38
3.2.2 幾何正規化......................................42
3.3 紋理特徵擷取.......................................43
3.3 以高斯混合模型擷取紋理特徵空間分布資訊.............50
3.4 特徵分類辨識方法...................................54
第四章 實驗結果與分析...................................58
4.1 實驗環境...........................................58
4.2 實驗結果...........................................59
第五章 結論.............................................63

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