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人脸多样化特征及其应用（英文版）Facial Multi-Characteristics and Applications
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人脸多样化特征及其应用（英文版）Facial Multi-Characteristics and Applications
样章
作者：
Bob Zhang, Qijun Zhao, David Zhang
定价：
119.00 元
版面字数：
580.00千字
开本：
16开
装帧形式：
精装
页数：
412
最新
印次时间：
2018年9月
ISBN：
978-7-04-049447-1
物料号：
49447-00
出版时间：
2018年10月
读者对象：
学术著作
一级分类：
自然科学
二级分类：
计算机科学与工程
三级分类：
人工智能
购买：
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图书目录
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目录
前辅文
Chapter 1 Introduction
1.1 Why Faces with Multi-Characteristics
1.2 Facial Authentication Using Permanent Special Features
1.3 Facial Beauty Analysis Using Permanent Common Features
1.4 Facial Diagnosis by Disease Changed Features
1.5 Expression Recognition by Stimulus Changed Features
1.6 Outline of This Book
References
PART I FACIAL AUTHENTICATION
Chapter 2 Facial Authentication Overview
2.1 Introduction
2.1.1 History of Automated Facial Recognition Research
2.1.2 Classification of Facial Recognition Scenarios
2.1.3 Challenges in Automated Facial Recognition
2.2 Permanent Unique Features for Facial Recognition
2.2.1 Geometric Features
2.2.2 Appearance Features
2.3 Facial Recognition: Systems and Applications
2.3.1 Major Modules in Automated Facial Recognition Systems
2.3.2 Application Modes
2.4 Chapters Overview
2.5 Summary
References
Chapter 3 Evolutionary Discriminant Feature Based Facial Recognition
3.1 Introduction
3.2 Evolutionary Discriminant Feature Extraction
3.2.1 Data Preprocessing: Centralization and Whitening
3.2.2 Calculating the Constrained Search Space
3.2.3 Searching: An Evolutionary Approach
3.2.4 Bagging EDFE
3.3 Facial Recognition Experiments
3.3.1 Databases and Parameter Settings
3.3.2 Investigation on Different Subspaces
3.3.3 Investigation on Dimensionality of Feature Subspaces
3.3.4 Performance Comparison
3.3.5 Discussion
3.4 Summary
References
Chapter 4 Facial Identification by Gabor Feature Based Robust Representation
4.1 Introduction
4.2 Related Work
4.2.1 Sparse Representation Based Classification (SRC)
4.2.2 Collaborative Representation Based Classification (CRC)
4.2.3 Gabor Features
4.3 Gabor-Feature Based Robust Representation and Classification
4.3.1 Gabor-Feature Based Robust Representation
4.3.2 Discussion on Occlusion Dictionary
4.3.3 Gabor Occlusion Dictionary (GOD) Computing
4.3.4 GRR Based Classification (GRRC)
4.3.5 Time Complexity
4.4 Experimental Results
4.4.1 Gabor Features and Regularization of GOD Computing
4.4.2 Face Recognition with Little Deformation
4.4.3 Face Recognition with Pose and Expression Variations
4.4.4 Recognition Against Occlusion
4.5 Discussion of Regularization on Coding Coefficients
4.6 Summary
References
Chapter 5 Three Dimension Enhanced Facial Identification
5.1 Introduction
5.2 Joint Face Alignment and 3D Face Reconstruction
5.2.1 Overview
5.2.2 Training Data Preparation
5.2.3 Learning Landmark Regressors
5.2.4 Estimating 3D-to-2D Mapping and Landmark Visibility
5.3 Application to Face Recognition
5.4 Experiments
5.4.1 Protocols
5.4.2 3D Face Reconstruction Accuracy
5.4.3 Face Alignment Accuracy
5.4.4 Face Recognition Accuracy
5.4.5 Computational Efficiency
5.5 Summary
References
PART II FACIAL BEAUTY ANALYSIS
Chapter 6 Facial Beauty Analysis Overview
6.1 Introduction
6.2 Permanent Common Features for Beauty Analysis
6.2.1 Golden Ratio Rules
6.2.2 Three Court Five
6.2.3 Averageness Hypothesis
6.2.4 Facial Symmetry and Beauty Perception
6.3 Facial Beauty Analysis: Features and Systems
6.3.1 Facial Feature Extraction
6.3.2 Modeling Methods
6.3.3 Applications
6.4 Chapters Overview
6.5 Summary
References
Chapter 7 Facial Beauty Analysis by Geometric Features
7.1 Introduction
7.2 Related Work
7.2.1 Facial Geometric Representation
7.2.2 Supervised Facial Beauty Model
7.2.3 Facial Attractiveness Assessment
7.3 Proposed Geometric Beauty Analysis Framework
7.3.1 Geometric Beauty Analysis
7.3.2 Hessian Regularization
7.3.3 Hessian Semi-Supervised Learning with Random Projection
7.3.4 Computational Complexity
7.3.5 Remarks on the Convergence
7.4 Experiments on the Proposed Dataset
7.4.1 Our Established Dataset
7.4.2 Low-Dimensional Distribution Visualization
7.4.3 Experimental Results
7.4.4 Verification
7.4.5 Insightful Implications
7.5 Experiment on M2B Dataset
7.5.1 M2B Dataset
7.5.2 Beauty Score Prediction
7.6 Discussion
7.7 Summary
References
Chapter 8 Facial Beauty Analysis by Landmark Model
8.1 Introduction
8.2 Related Work
8.3 Key Point (KP) Definition
8.4 Inserted Point (IP) Generation
8.4.1 Quantitative Measure of the Precision of LMs
8.4.2 Iterative Search for Optimal Positions of IPs
8.5 The Optimized Landmark Model
8.5.1 Training Data Preparation
8.5.2 Generation and the Optimized LM
8.6 Comparison with Other LMs
8.6.1 Comparison of Approximation Error
8.6.2 Comparison of Landmark Detection Error
8.7 Applications
8.7.1 Facial Beauty Analysis
8.7.2 Facial Animation
8.8 Summary
References
Chapter 9 A New Hypothesis for Facial Beauty Analysis
9.1 Introduction
9.2 Notations and the New Hypothesis
9.3 Empirical Proof of the WA Hypothesis
9.3.1 Face Image Dataset
9.3.2 Attractiveness Score Collection
9.3.3 Attractiveness Score Regression
9.3.4 Testing the Hypothesis
9.4 Corollary of the Hypothesis and Convex Hull-Based Face Beautification
9.4.1 Corollary of the WA Hypothesis
9.4.2 Convex Hull-Based Face Beautification
9.4.3 Results
9.4.4 Comparison and Discussion
9.5 Compatibility with Other Hypotheses
9.5.1 Compatibility with the Averageness Hypothesis
9.5.2 Compatibility with the Symmetry Hypothesis
9.5.3 Compatibility with the Golden Ratio Hypothesis
9.6 Summary
References
Chapter 10 Facial Beauty Analysis: Prediction, Retrieval and Manipulation
10.1 Introduction
10.2 Facial Image Preprocessing and Feature Extraction
10.2.1 Face Detection and Landmark Extraction
10.2.2 Face Registration and Cropping
10.2.3 Feature Extraction
10.3 Facial Beauty Modeling
10.3.1 Problem Formulation
10.3.2 Regression Methods
10.4 Facial Beauty Prediction
10.5 Beauty-Oriented Face Retrieval
10.5.1 Retrieval for Face Recommendation
10.5.2 Retrieval for Face Beautification
10.6 Facial Beauty Manipulation
10.6.1 Exemplar-Based Manipulation
10.6.2 Model-Based Manipulation
10.7 Experiments
10.7.1 Data Set
10.7.2 Evaluation of Features for Facial Beauty Prediction
10.7.3 Benefit of Soft Biometric Traits
10.7.4 Results of Feature Fusion and Selection
10.7.5 Results of Beauty-Oriented Face Retrieval
10.7.6 Results of Facial Beauty Manipulation
10.8 Summary
References
PART III FACIAL DIAGNOSIS
Chapter 11 Facial Diagnosis Overview
11.1 Introduction
11.2 Disease Changing Features for Facial Diagnosis
11.3 Computerized Facial Diagnosis
11.4 Chapters Overview
11.5 Summary
References
Chapter 12 Non-Invasive Diabetes Mellitus Detection Using Facial Colors
12.1 Introduction
12.2 Facial Images and Dataset
12.2.1 Facial Image Acquisition Device
12.2.2 Facial Block Definition
12.2.3 Facial Image Dataset
12.3 Facial Block Color Feature Extraction
12.4 Healthy Versus DM Classification with the SRC
12.5 Experimental Results
12.6 Discussion
12.7 Summary
References
Chapter 13 Health Status Analysis by Facial Texture Features
13.1 Introduction
13.2 Facial Image Acquisition Device
13.3 Facial Image Pre-Processing and the Dataset
13.4 Facial Image Texture Features Extraction
13.5 Classification
13.6 Experiments
13.7 Summary
References
Chapter 14 Computerized Facial Diagnosis Using Both Color and Texture Features
14.1 Introduction
14.2 Facial Image Dataset
14.2.1 Facial Image Acquisition Device
14.2.2 Facial Image Dataset
14.2.3 Facial Block Definition
14.3 Facial Feature Extraction
14.3.1 Color Feature Using Space Distribution
14.3.2 Texture Feature Extracted by Gabor Filter
14.4 Healthy Classification Using Facial Gloss
14.5 Facial Block-Based Disease Analysis
14.5.1 Diagnosis Using Single Block
14.5.2 Optimal Blocks Combination
14.6 Summary
References
PART IV FACIAL EXPRESSION RECOGNITION
Chapter 15 Expression Recognition Overview
15.1 Introduction
15.1.1 Description of Facial Expressions
15.1.2 Modalities in Facial Expression Recognition
15.1.3 History of Facial Expression Recognition Research
15.1.4 Challenges in Facial Expression Recognition
15.2 Stimulus Changed Features for Expression Recognition
15.2.1 Geometric vs. Appearance Features
15.2.2 Global vs. Local Features
15.2.3 Static vs. Dynamic Features
15.2.4 Hand-crafted vs. Learned Features
15.3 Facial Expression Recognition: Systems and Applications
15.3.1 Workflow in Facial Expression Recognition Systems
15.3.2 Applications
15.4 Chapters Overview
15.5 Summary
References
Chapter 16 Expression Recognition by Supervised LLE
16.1 Introduction
16.2 Independent Component Analysis
16.3 Supervised Locally Linear Embedding
16.4 Experiments
16.4.1 Testing Methodology
16.4.2 Experimental Results
16.5 Summary
References
Chapter 17 Expression Recognition on Multiple Manifolds
17.1 Introduction
17.2 Multi-Manifold Based Facial Expression Recognition
17.2.1 Learning Expression Manifolds
17.2.2 Multi-Manifold Based Classification
17.2.3 Dimensionality Selection
17.2.4 The Procedures
17.3 Experiments and Discussion
17.3.1 Data Sets
17.3.2 Feature Extraction
17.3.3 Experimental Results
17.3.4 Discussion
17.4 Summary
References
Chapter 18 Cross Domain Facial Expression Recognition
18.1 Introduction
18.2 A Transfer Learning Based Approach
18.2.1 Problem Formulation
18.2.2 Overview of the Approach
18.2.3 Training with Transfer Learning
18.2.4 Evaluation Experiments
18.3 A Discriminative Feature Adaptation Approach
18.3.1 Problem Formulation
18.3.2 Domain Matching
18.3.3 Discriminative Analysis
18.3.4 Optimization for DFA
18.3.5 Evaluation Experiments
18.3.6 Results and Discussion
18.4 Summary
References
Chapter 19 Book Review and Future Work
19.1 Book Recapitulation
19.2 Challenges and Future Work
Index
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