臺灣博碩士論文加值系統

本論文將分析與比較人臉辨識系統中常見的一些理論與方法的優缺點，除了從巨觀的觀點來分析人臉辨識外，有關人臉辨識的相關方法有：主成分分析(Principle Component Analysis, PCA)、獨立成分分析(Independent Component Analysis, ICA)、線性判斷分析(Linear Discriminant Analysis, LDA)、隱藏馬可夫模型(Hidden-Markov Models, HMM)、支持向量機等方法(Support Vector Machines, SVM)。最後，也討論於2010年我們所發表的【基於不均勻度特徵及K-L轉換之生物辨識：應用於人臉辨識】，該研究中是先以影像的不均勻度(Gini index)的值提取影像中人臉辨識重要的部分，利用KLT截取其特徵，再利用這個特徵當作模版進行辨識；最後，使用Otsu法決定各候選影像與模版的KLT歐幾里德距離的最佳辨識門檻值。根據此方法的實驗結果，本方法可在維持相似的辨識率的前提下，提升人臉辨識速度一倍以上。

This thesis reviews and compares the pros and cons of several popular theories and methods for face recognize system, such as PCA, ICA, LDA, HMM, SVM, etc. In the end, the thesis also presents our study of “Face Recognition Base on Gini Features and K-L Transform” which was published in ITIA 2010 conference. This study is to improve the performance of Karhunen-Loève transform (KLT) in face recognition of biometrics. A measure of non-uniformity, called Gini index, is used to extract critical blocks of a human face so that the computation needed can be reduced with satisfactory recognition accuracy. According to our experimental results, this approach can accelerate face recognizing process for two-fold with similar accuracy.

誌謝 I
摘要 II
Abstract III
目錄 IV
圖目錄 VII
表目錄 VIII
第一章 人臉辨識 1
1.1. 前言 1
1.2. 什麼是人臉辨識？ 1
1.3. 人臉辨識的優勢 2
1.4. 人臉辨識的困難 3
第二章 人臉資料庫 5
2.1. AT&T人臉資料庫(ORL人臉資料庫) 5
2.2. Yale 人臉資料庫 6
2.3. INDIAN人臉資料庫 7
第三章 主成分分析(Principal Comonents Analysis,PCA) 9
3.1. 前言 9
3.2. 方法 9
第四章 獨立成分分析(Independent Component Analysis, ICA) 15
4.1. 前言 15
4.2. 方法 15
第五章 線性鑑別分析(Linear Discriminant Analysis, LDA) 18
5.1. 前言 18
5.2. 方法 18
第六章 隱藏馬可夫模型(Hidden Markov Models, HMM) 22
6.1. 前言 22
6.2. 方法 22
6.2.1. HMM的基本方法 22
6.2.2. HMM應用於人臉辨識 25
第七章 支持向量機(Support vector machine, SVM) 29
7.1. 前言 29
7.2. 方法 29
第八章 基於不均勻度特徵及K-L轉換 34
8.1. 前言 34
8.2. 方法 34
8.2.1. Gini Index的選定 35
8.2.2. Otsu演算法 36
8.3. 方法流程及結果 38
第九章 方法比較與結論 43
9.1. 主成分分析(PCA或稱KLT) 43
9.2. 獨立成分分析(ICA) 43
9.3. 線性鑑別分析(LDA) 44
9.4. 隱藏式馬可夫模型(HMM) 44
9.5. 支援向量機(SVM) 45
9.6. 基於不均勻度特徵及K-L轉換 45
9.7. 總結 46
第十章 未來展望 48
參考文獻 49

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