FACIAL MULTI-CHARACTERISTICS AND APPLICATIONS
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| Format: | Electronic Book |
| Language: | English |
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[S.l.] :
WORLD SCIENTIFIC PUB,
2018
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Table of Contents:
- Intro; Contents; PREFACE; 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 Recognition2.2 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 Approach3.2.4 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 Classification4.3.1 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; 4.4.1.1 Gabor Features; 4.4.1.2 Regularization on GOD Computing; 4.4.2 Face Recognition with Little Deformation; 4.4.2.1 Extended Yale B Database; 4.4.2.2 AR Database; 4.4.2.3 Multi-PIE Database; 4.4.2.4 FERET Database
- 4.4.3 Face Recognition with Pose and Expression Variations4.4.3.1 FERET Pose Database; 4.4.3.2 Multi-PIE Expression Subset; 4.4.4 Recognition Against Occlusion; 4.4.4.1 FR with Random Block Occlusion; 4.4.4.2 FR with Real Disguise; 4.4.4.3 Running Time Comparison; 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; 5.2.1.1 Updating Landmarks; 5.2.1.2 Updating 3D Face Shape; 5.2.1.3 Refining Landmarks