包邮 博士后文库复杂条件下的人脸特征提取和分类研究(英文版)

《博士后文库》序言 Preface Chapter 1 Introduction 1.1 Research Significance 1.2 Current Research Situation 1.2.1 Methods Based on Geometric Feature 1.2.2 Methods Based on Subspace Analysis 1.2.3 Methods Based on Machine Learning 1.2.4 Methods Based on Model 1.2.5 Methods Based on Local Feature 1.3 Classification Rules in Image Recognition 1.4 Difficulties of Face Recognition 1.5 Face Recognition System References Chapter 2 Image Synthesis and Classification Method Based on Quotient Image Theory 2.1 Introduction 2.2 Background Review 2.2.1 The Quotient Image Theory 2.2.2 Illumination Subspace 2.3 The Quotient Image Method Based on 9-dimension Linear Subspace 2.3.1 The Improved Quotient Image Method 2.3.2 Basis Image Synthesis Method 2.3.3 Illumination Direction Estimation 2.4 The Review of PCA 2.5 Face Recognition Under Different Lighting Conditions 2.6 Experiments and Results 2.6.1 The Quotient Image 2.6.2 Nine Basis Images Reconstruction 2.6.3 Face Recognition Under Varying Illumination ; 2.7 Conclusions References Chapter 3 A Classification Method Based on Reconstruction Error and Normalized Distance 3.1 Introduction 3.2 Main Steps of Fusion Method Based on Reconstruction Error and Normalized Distance 3.3 Potential Rationale of the Method 3.4 Experiments and Results 3.4.1 Experiments on the Po1yU Palmprint Database 3.4.2 Experiments on the 2D+3D Palmprint Database 3.4.3 Experiments on Corrupted Palmprint Images 3.5 Conclusions References Chapter 4 Integrating the Original and Approximate Face Images to Perform Collaborative Representation Based Classification. 4.1 Introduction 4.2 Collaborative Representation Based Classification (CRC) 4.3 The Proposed Method 4.4 Experiments and Results 4.4.1 The Approximate Face Image 4.4.2 Experiments on ORL Face Database 4.4.3 Experiments on Yale Face Database 4.4.4 Experiments on FERET Face Database 4.4.5 Experiments on AR Face Database 4.5 Conclusions References Chapter 5 Using the Original and Symmetrical Face Training Samples to Perform Collaborative Representation 5.1 Introduction 5.2 Collaborative Representation Based Classification(CRC) 5.3 The Proposed Method 5.4 Experiments and Results 5.4.1 The Symmetrical Face Image 5.4.2 Experiments on ORL Face Database 5.4.3 Experiments on Yale Face Database 5.4.4 Experiments on AR Face Database 5.5 Conclusions References Chapter 6 A Enhanced Collaborative Representation Based Classification Method 6.1 Introduction 6.2 Collaborative Representation Based Classification (CRC) 6.3 Enhanced Collaborative Representation Based Classification (ECRC) 6.4 Experiments and Results 6.4.1 Experiments on ORL Face Database 6.4.2 Experiments on Yale Face Database" 6.4.3 Experiments on FERET Face Database 6.5 Conclusions References Chapter 7 AApproximate and Competitive Representation Method with One sample Per Person 7.1 Introduction 7.2 Main Steps of Approximate and Competitive Representation Method 7.3 Potential Rationale of Our Method 7.4 Experiments and Results 7.4.1 Face Databases 7.4.2 Experimental Results 7.5 Conclusions References Chapter 8 A Kernel Twos-Phase Test Sample Sparse Representation Method 8.1 Introduction 8.2 Two-Phase Test Sample Sparse Representation (TPTSSR) 8.3 Kernel Two-Phase Test Sample Sparse Representation (KTPTSSR) 8.4 Experiments and Results 8.4.1 Experiments on ORL Face Database 8.4.2 Experiments on AR Face Database 8.4.3 Experiments on Yale Face Database 8.4.4 Experiments on FERET Face Database 8.5 Conclusions References Chapter 9 A Weighted Two-Phase Test Sample Sparse Representation Method 9.1 Introduction 9.2 Two-Phase Test Sample Sparse Representation (TPTSSR) 9.3 Weighted Two-Phase Test Sample Sparse Representation (WTPTSSR) 9.4 Experiments and Results 9.4.1 Selection of Parameter 9.4.2 Face Recognition Experiments 9.5 Conclusions References Chapter 10 Quaternion-based Maximum Margin Criterion Method for Color Image Recognition 10.1 Introduction 10.2 Quaternion-based Maximum Margin Criterion (QMMC) 10.2.1 Maximum Margin Criterion 10.2.2 Quaternion-based Color Image Representation 10.2.3 Quaternion-based Maximum Margin Criterion Algorithm 10.3 Experiments and Results 10.3.1 Experiments on AR Face Database 10.3.2 Experiments on Georgia Tech Face Database 10.3.3 Experiments on LFW Face Database 10.4 Conclusions References