0去购物车结算
购物车中还没有商品，赶紧选购吧！
当前位置: > Research on Face Feature Extraction and Classification Algorithms Under Complex Conditions 复杂条件下的人脸特征提取 和分类算法研究

相同作者的商品

相同语种的商品

销售排行榜

浏览历史

Research on Face Feature Extraction and Classification Algorithms Under Complex Conditions 复杂条件下的人脸特征提取 和分类算法研究


联系编辑
 
标题：
 
内容：
 
联系方式：
 
  
Research on Face Feature Extraction and Classification Algorithms Under Complex Conditions 复杂条件下的人脸特征提取 和分类算法研究
  • 书号：9787030549501
    作者：刘中华
  • 外文书名：
  • 装帧：平装
    开本：B5
  • 页数：136
    字数：189000
    语种：en
  • 出版社：科学出版社
    出版时间：2018-06-01
  • 所属分类：
  • 定价： ￥78.00元
    售价： ￥61.62元
  • 图书介质:
    按需印刷

  • 购买数量： 件  缺货，请选择其他介质图书！
  • 商品总价：

相同系列
全选

内容介绍

样章试读

用户评论

全部咨询

As motivated by the extensive potential applications in public security, financial security, human-computer interaction, biometrics recognition especially face recognition has become one of the hot topics in the fields of pattern recognition and artificial intelligence. This book mainly focuses on feature extraction and classification algorithms under complex conditions, and its objective is that the readers can quickly understand the latest feature extraction and classification methods. The main contents of the book are as follows: image synthesis and classification method based on quotient image theory; a classification method based on reconstruction error and normalized distance; face recognition method based on the original and approximate face images; enhanced collaborative representation based classification; approximate and competitive representation method; a kernel two-phase test sample sparse representation method; quaternion-based maximum margin criterion method.
　　This book can be used for postgraduates and senior undergraduates majored in control science and engineering. Meanwhile, it is also a quite useful reference book for the researchers in the related fields.
样章试读
  • 暂时还没有任何用户评论
总计 0 个记录，共 1 页。 第一页 上一页 下一页 最末页

全部咨询(共0条问答)

  • 暂时还没有任何用户咨询内容
总计 0 个记录，共 1 页。 第一页 上一页 下一页 最末页
用户名： 匿名用户
E-mail：
咨询内容：

目录

  • Contents
    《博士后文库》序言
    Preface
    Chapter 1 Introduction 1
    1.1 Research Significance 1
    1.2 Current Research Situation 2
    1.2.1 Methods Based on Geometric Feature 3
    1.2.2 Methods Based on Subspace Analysis 3
    1.2.3 Methods Based on Machine Learning 6
    1.2.4 Methods Based on Model 7
    1.2.5 Methods Based on Local Feature 8
    1.3 Classification Rules in Image Recognition 9
    1.4 Difficulties of Face Recognition 10
    1.5 Face Recognition System 11
    References 14
    Chapter 2 Image Synthesis and Classification Method Based on Quotient Image Theory 19
    2.1 Introduction 19
    2.2 Background Review 20
    2.2.1 The Quotient Image Theory 20
    2.2.2 Illumination Subspace 22
    2.3 The Quotient Image Method Based on 9-dimension Linear Subspace 23
    2.3.1 The Improved Quotient Image Method 24
    2.3.2 Basis Image Synthesis Method 24
    2.3.3 Illumination Direction Estimation 25
    2.4 The Review of PCA 26
    2.5 Face Recognition Under Different Lighting Conditions 26
    2.6 Experiments and Results 27
    2.6.1 The Quotient Image 27
    2.6.2 Nine Basis Images Reconstruction 28
    2.6.3 Face Recognition Under Varying Illumination 29
    2.7 Conclusions 30
    References 30
    Chapter 3 A Classification Method Based on Reconstruction Error and Normalized Distance 33
    3.1 Introduction 33
    3.2 Main Steps of Fusion Method Based on Reconstruction Error and Normalized Distance 36
    3.3 Potential Rationale of the Method 38
    3.4 Experiments and Results 43
    3.4.1 Experiments on the PolyU Palmprint Database 43
    3.4.2 Experiments on the 2D+3D Palmprint Database 44
    3.4.3 Experiments on Corrupted Palmprint Images 44
    3.5 Conclusions 45
    References 45
    Chapter 4 Integrating the Original and Approximate Face Images to Perform Collaborative Representation Based Classification 50
    4.1 Introduction 50
    4.2 Collaborative Representation Based Classification (CRC) 52
    4.3 The Proposed Method 53
    4.4 Experiments and Results 55
    4.4.1 The Approximate Face Image 55
    4.4.2 Experiments on ORL Face Database 56
    4.4.3 Experiments on Yale Face Database 56
    4.4.4 Experiments on FERET Face Database 58
    4.4.5 Experiments on AR Face Database 59
    4.5 Conclusions 60
    References 61
    Chapter 5 Using the Original and Symmetrical Face Training Samples to Perform Collaborative Representation 63
    5.1 Introduction 63
    5.2 Collaborative Representation Based Classification(CRC) 65
    5.3 The Proposed Method 67
    5.4 Experiments and Results 68
    5.4.1 The Symmetrical Face Image 68
    5.4.2 Experiments on ORL Face Database 69
    5.4.3 Experiments on Yale Face Database 70
    5.4.4 Experiments on AR Face Database 71
    5.5 Conclusions 73
    References 73
    Chapter 6 A Enhanced Collaborative Representation Based Classification Method 76
    6.1 Introduction 76
    6.2 Collaborative Representation Based Classification (CRC) 77
    6.3 Enhanced Collaborative Representation Based Classification (ECRC) 78
    6.4 Experiments and Results 79
    6.4.1 Experiments on ORL Face Database 79
    6.4.2 Experiments on Yale Face Database 81
    6.4.3 Experiments on FERET Face Database 82
    6.5 Conclusions 82
    References 83
    Chapter 7 A Approximate and Competitive Representation Method with One sample Per Person 85
    7.1 Introduction 85
    7.2 Main Steps of Approximate and Competitive Representation Method 87
    7.3 Potential Rationale of Our Method 88
    7.4 Experiments and Results 89
    7.4.1 Face Databases 89
    7.4.2 Experimental Results 91
    7.5 Conclusions 92
    References 92
    Chapter 8 A Kernel Twos-Phase Test Sample Sparse Representation Method 95
    8.1 Introduction 95
    8.2 Two-Phase Test Sample Sparse Representation (TPTSSR) 97
    8.3 Kernel Two-Phase Test Sample Sparse Representation (KTPTSSR) 98
    8.4 Experiments and Results 100
    8.4.1 Experiments on ORL Face Database 101
    8.4.2 Experiments on AR Face Database 102
    8.4.3 Experiments on Yale Face Database 104
    8.4.4 Experiments on FERET Face Database 105
    8.5 Conclusions 106
    References 107
    Chapter 9 A Weighted Two-Phase Test Sample Sparse Representation Method 110
    9.1 Introduction 110
    9.2 Two-Phase Test Sample Sparse Representation (TPTSSR) 112
    9.3 Weighted Two-Phase Test Sample Sparse Representation (WTPTSSR) 113
    9.4 Experiments and Results 115
    9.4.1 Selection of Parameter 115
    9.4.2 Face Recognition Experiments 116
    9.5 Conclusions 121
    References 121
    Chapter 10 Quaternion-based Maximum Margin Criterion Method for Color Image Recognition 124
    10.1 Introduction 124
    10.2 Quaternion-based Maximum Margin Criterion (QMMC) 126
    10.2.1 Maximum Margin Criterion 126
    10.2.2 Quaternion-based Color Image Representation 126
    10.2.3 Quaternion-based Maximum Margin Criterion Algorithm 127
    10.3 Experiments and Results 129
    10.3.1 Experiments on AR Face Database 129
    10.3.2 Experiments on Georgia Tech Face Database 131
    10.3.3 Experiments on LFW Face Database 132
    10.4 Conclusions 134
    References 134
    编后记 137
帮助中心
公司简介
联系我们
常见问题
新手上路
发票制度
积分说明
购物指南
配送方式
配送时间及费用
配送查询说明
配送范围
快递查询
售后服务
退换货说明
退换货流程
投诉或建议
版权声明
经营资质
营业执照
出版社经营许可证

中国科技出版传媒股份有限公司　版权所有

　

京ICP备14028887号-5　 京ICP证150976号 京公网安备 11010102004214号

北京东黄城根北街16号 邮编：100717 Email:webmaster@mail.sciencep.com