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E-raamat: Facial Multi-characteristics And Applications

(The Chinese University Of Hong Kong, Shenzhen, China), (Sichuan Univ, China), (University Of Macau, China)
  • Formaat: 428 pages
  • Ilmumisaeg: 19-Nov-2018
  • Kirjastus: World Scientific Publishing Co Pte Ltd
  • Keel: eng
  • ISBN-13: 9789813234598
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Facial Multi-characteristics And ApplicationsSuurem pilt
  • Formaat: 428 pages
  • Ilmumisaeg: 19-Nov-2018
  • Kirjastus: World Scientific Publishing Co Pte Ltd
  • Keel: eng
  • ISBN-13: 9789813234598
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What features or information can we observe from a face, and how can these information help us to understand the person concerned, in terms of their well-being and what can we learn about and from each given feature? This book answers these questions by first dividing a face's multiple characteristics into two main categories: original (or physiological) features and features that change over a lifetime. The first category, original features, may be further divided into two sub-classes: features special (or unique) to an individual, and features common to a particular group. The second, changed features, can also be subdivided into two groups: features altered due to disease or features altered by other external factors. From these four sub-categories, four different applications facial identification using original and special features; beauty analysis using original common features; facial diagnosis by disease changed features; and expression recognition through affect-changed features are identified.The book will benefit researchers, professionals, and graduate students working in the field of computer vision, pattern recognition, security/clinical practice, and beauty analysis, and will also be useful for interdisciplinary research.
Chapter 1 Introduction 1(16)
1.1 Why Faces with Multi-Characteristics
1(1)
1.2 Facial Authentication Using Permanent Special Features
2(1)
1.3 Facial Beauty Analysis Using Permanent Common Features
3(3)
1.4 Facial Diagnosis by Disease Changed Features
6(1)
1.5 Expression Recognition by Stimulus Changed Features
6(1)
1.6 Outline of This Book
7(2)
References
9(8)
Part I: Facial Authentication
Chapter 2 Facial Authentication Overview
17(22)
2.1 Introduction
17(7)
2.1.1 History of Automated Facial Recognition Research
17(2)
2.1.2 Classification of Facial Recognition Scenarios
19(2)
2.1.3 Challenges in Automated Facial Recognition
21(3)
2.2 Permanent Unique Features for Facial Recognition
24(3)
2.2.1 Geometric Features
24(1)
2.2.2 Appearance Features
25(2)
2.3 Facial Recognition: Systems and Applications
27(4)
2.3.1 Major Modules in Automated Facial Recognition Systems
27(2)
2.3.2 Application Modes
29(2)
2.4
Chapters Overview
31(1)
2.5 Summary
31(1)
References
32(7)
Chapter 3 Evolutionary Discriminant Feature Based Facial Recognition
39(26)
3.1 Introduction
39(3)
3.2 Evolutionary Discriminant Feature Extraction
42(10)
3.2.1 Data Preprocessing: Centralization and Whitening
43(1)
3.2.2 Calculating the Constrained Search Space
43(2)
3.2.3 Searching: An Evolutionary Approach
45(4)
3.2.4 Bagging EDFE
49(3)
3.3 Facial Recognition Experiments
52(9)
3.3.1 Databases and Parameter Settings
52(2)
3.3.2 Investigation on Different Subspaces
54(1)
3.3.3 Investigation on Dimensionality of Feature Subspaces
55(4)
3.3.4 Performance Comparison
59(1)
3.3.5 Discussion
60(1)
3.4 Summary
61(1)
References
62(3)
Chapter 4 Facial Identification by Gabor Feature Based Robust Representation
65(34)
4.1 Introduction
65(4)
4.2 Related Work
69(2)
4.2.1 Sparse Representation Based Classification (SRC)
69(1)
4.2.2 Collaborative Representation Based Classification (CRC)
70(1)
4.2.3 Gabor Features
70(1)
4.3 Gabor-Feature Based Robust Representation and Classification
71(7)
4.3.1 Gabor-Feature Based Robust Representation
71(1)
4.3.2 Discussion on Occlusion Dictionary
72(2)
4.3.3 Gabor Occlusion Dictionary (GOD) Computing
74(2)
4.3.4 GRR Based Classification (GRRC)
76(1)
4.3.5 Time Complexity
77(1)
4.4 Experimental Results
78(15)
4.4.1 Gabor Features and Regularization of GOD Computing
79(2)
4.4.2 Face Recognition with Little Deformation
81(4)
4.4.3 Face Recognition with Pose and Expression Variations
85(3)
4.4.4 Recognition Against Occlusion
88(5)
4.5 Discussion of Regularization on Coding Coefficients
93(1)
4.6 Summary
94(1)
References
94(5)
Chapter 5 Three Dimension Enhanced Facial Identification
99(24)
5.1 Introduction
99(4)
5.2 Joint Face Alignment and 3D Face Reconstruction
103(5)
5.2.1 Overview
103(1)
5.2.2 Training Data Preparation
104(2)
5.2.3 Learning Landmark Regressors
106(1)
5.2.4 Estimating 3D-to-2D Mapping and Landmark Visibility
107(1)
5.3 Application to Face Recognition
108(1)
5.4 Experiments
109(6)
5.4.1 Protocols
109(2)
5.4.2 3D Face Reconstruction Accuracy
111(1)
5.4.3 Face Alignment Accuracy
112(2)
5.4.4 Face Recognition Accuracy
114(1)
5.4.5 Computational Efficiency
115(1)
5.5 Summary
115(1)
References
116(7)
Part II: Facial Beauty Analysis
Chapter 6 Facial Beauty Analysis Overview
123(22)
6.1 Introduction
123(6)
6.2 Permanent Common Features for Beauty Analysis
129(6)
6.2.1 Golden Ratio Rules
129(2)
6.2.2 Three Court Five
131(1)
6.2.3 Averageness Hypothesis
131(2)
6.2.4 Facial Symmetry and Beauty Perception
133(2)
6.3 Facial Beauty Analysis: Features and Systems
135(2)
6.3.1 Facial Feature Extraction
135(1)
6.3.2 Modeling Methods
136(1)
6.3.3 Applications
137(1)
6.4
Chapters Overview
137(2)
6.5 Summary
139(1)
References
140(5)
Chapter 7 Facial Beauty Analysis by Geometric Features
145(34)
7.1 Introduction
145(3)
7.2 Related Work
148(4)
7.2.1 Facial Geometric Representation
148(2)
7.2.2 Supervised Facial Beauty Model
150(1)
7.2.3 Facial Attractiveness Assessment
151(1)
7.3 Proposed Geometric Beauty Analysis Framework
152(5)
7.3.1 Geometric Beauty Analysis
152(1)
7.3.2 Hessian Regularization
153(1)
7.3.3 Hessian Semi-Supervised Learning with Random Projection
154(2)
7.3.4 Computational Complexity
156(1)
7.3.5 Remarks on the Convergence
156(1)
7.4 Experiments on the Proposed Dataset
157(13)
7.4.1 Our Established Dataset
157(3)
7.4.2 Low-Dimensional Distribution Visualization
160(1)
7.4.3 Experimental Results
161(4)
7.4.4 Verification
165(4)
7.4.5 Insightful Implications
169(1)
7.5 Experiment on M2B Dataset
170(2)
7.5.1 M2B Dataset
170(1)
7.5.2 Beauty Score Prediction
171(1)
7.6 Discussion
172(2)
7.7 Summary
174(1)
References
174(5)
Chapter 8 Facial Beauty Analysis by Landmark Model
179(22)
8.1 Introduction
179(4)
8.2 Related Work
183(1)
8.3 Key Point (KP) Definition
184(2)
8.4 Inserted Point (IP) Generation
186(3)
8.4.1 Quantitative Measure of the Precision of LMs
186(1)
8.4.2 Iterative Search for Optimal Positions of IPs
187(2)
8.5 The Optimized Landmark Model
189(4)
8.5.1 Training Data Preparation
189(1)
8.5.2 Generation and the Optimized LM
189(4)
8.6 Comparison with Other LMs
193(4)
8.6.1 Comparison of Approximation Error
193(2)
8.6.2 Comparison of Landmark Detection Error
195(2)
8.7 Applications
197(2)
8.7.1 Facial Beauty Analysis
197(1)
8.7.2 Facial Animation
198(1)
8.8 Summary
199(1)
References
199(2)
Chapter 9 A New Hypothesis for Facial Beauty Analysis
201(26)
9.1 Introduction
201(4)
9.2 Notations and the New Hypothesis
205(1)
9.3 Empirical Proof of the WA Hypothesis
206(7)
9.3.1 Face Image Dataset
207(1)
9.3.2 Attractiveness Score Collection
207(1)
9.3.3 Attractiveness Score Regression
208(1)
9.3.4 Testing the Hypothesis
209(4)
9.4 Corollary of the Hypothesis and Convex Hull-Based Face Beautification
213(7)
9.4.1 Corollary of the WA Hypothesis
213(1)
9.4.2 Convex Hull-Based Face Beautification
213(1)
9.4.3 Results
214(2)
9.4.4 Comparison and Discussion
216(4)
9.5 Compatibility with Other Hypotheses
220(2)
9.5.1 Compatibility with the Averageness Hypothesis
220(1)
9.5.2 Compatibility with the Symmetry Hypothesis
221(1)
9.5.3 Compatibility with the Golden Ratio Hypothesis
221(1)
9.6 Summary
222(1)
References
223(4)
Chapter 10 Facial Beauty Analysis: Prediction, Retrieval and Manipulation
227(26)
10.1 Introduction
227(4)
10.2 Facial Image Preprocessing and Feature Extraction
231(5)
10.2.1 Face Detection and Landmark Extraction
231(1)
10.2.2 Face Registration and Cropping
232(1)
10.2.3 Feature Extraction
232(4)
10.3 Facial Beauty Modeling
236(1)
10.3.1 Problem Formulation
236(1)
10.3.2 Regression Methods
236(1)
10.4 Facial Beauty Prediction
236(2)
10.5 Beauty-Oriented Face Retrieval
238(1)
10.5.1 Retrieval for Face Recommendation
238(1)
10.5.2 Retrieval for Face Beautification
238(1)
10.6 Facial Beauty Manipulation
239(2)
10.6.1 Exemplar-Based Manipulation
239(1)
10.6.2 Model-Based Manipulation
240(1)
10.7 Experiments
241(6)
10.7.1 Data Set
241(1)
10.7.2 Evaluation of Features for Facial Beauty Prediction
241(1)
10.7.3 Benefit of Soft Biometric Traits
242(1)
10.7.4 Results of Feature Fusion and Selection
243(1)
10.7.5 Results of Beauty-Oriented Face Retrieval
244(1)
10.7.6 Results of Facial Beauty Manipulation
245(2)
10.8 Summary
247(1)
References
248(5)
Part III: Facial Diagnosis
Chapter 11 Facial Diagnosis Overview
253(10)
11.1 Introduction
253(1)
11.2 Disease Changing Features for Facial Diagnosis
254(1)
11.3 Computerized Facial Diagnosis
254(4)
11.4
Chapters Overview
258(1)
11.5 Summary
259(1)
References
259(4)
Chapter 12 Non-Invasive Diabetes Mellitus Detection Using Facial Colors
263(14)
12.1 Introduction
263(2)
12.2 Facial Images and Dataset
265(2)
12.2.1 Facial Image Acquisition Device
265(1)
12.2.2 Facial Block Definition
266(1)
12.2.3 Facial Image Dataset
266(1)
12.3 Facial Block Color Feature Extraction
267(4)
12.4 Healthy Versus DM Classification with the SRC
271(2)
12.5 Experimental Results
273(2)
12.6 Discussion
275(1)
12.7 Summary
276(1)
References
276(1)
Chapter 13 Health Status Analysis by Facial Texture Features
277(14)
13.1 Introduction
277(1)
13.2 Facial Image Acquisition Device
278(1)
13.3 Facial Image Pre-Processing and the Dataset
279(2)
13.4 Facial Image Texture Features Extraction
281(2)
13.5 Classification
283(1)
13.6 Experiments
284(4)
13.7 Summary
288(1)
References
289(2)
Chapter 14 Computerized Facial Diagnosis Using Both Color and Texture Features
291(18)
14.1 Introduction
291(2)
14.2 Facial Image Dataset
293(2)
14.2.1 Facial Image Acquisition Device
293(1)
14.2.2 Facial Image Dataset
294(1)
14.2.3 Facial Block Definition
295(1)
14.3 Facial Feature Extraction
295(5)
14.3.1 Color Feature Using Space Distribution
295(4)
14.3.2 Texture Feature Extracted by Gabor Filter
299(1)
14.4 Healthy Classification Using Facial Gloss
300(3)
14.5 Facial Block-Based Disease Analysis
303(2)
14.5.1 Diagnosis Using Single Block
303(1)
14.5.2 Optimal Blocks Combination
304(1)
14.6 Summary
305(1)
References
306(3)
Part IV: Facial Expression Recognition
Chapter 15 Expression Recognition Overview
309(20)
15.1 Introduction
309(7)
15.1.1 Description of Facial Expressions
310(1)
15.1.2 Modalities in Facial Expression Recognition
311(1)
15.1.3 History of Facial Expression Recognition Research
311(3)
15.1.4 Challenges in Facial Expression Recognition
314(2)
15.2 Stimulus Changed Features for Expression Recognition
316(3)
15.2.1 Geometric vs. Appearance Features
316(2)
15.2.2 Global vs. Local Features
318(1)
15.2.3 Static vs. Dynamic Features
319(1)
15.2.4 Hand-crafted vs. Learned Features
319(1)
15.3 Facial Expression Recognition: Systems and Applications
319(2)
15.3.1 Workflow in Facial Expression Recognition Systems
319(1)
15.3.2 Applications
320(1)
15.4
Chapters Overview
321(1)
15.5 Summary
321(1)
References
321(8)
Chapter 16 Expression Recognition by Supervised LLE
329(10)
16.1 Introduction
329(2)
16.2 Independent Component Analysis
331(2)
16.3 Supervised Locally Linear Embedding
333(1)
16.4 Experiments
334(3)
16.4.1 Testing Methodology
335(1)
16.4.2 Experimental Results
335(2)
16.5 Summary
337(1)
References
338(1)
Chapter 17 Expression Recognition on Multiple Manifolds
339(22)
17.1 Introduction
339(2)
17.2 Multi-Manifold Based Facial Expression Recognition
341(8)
17.2.1 Learning Expression Manifolds
341(3)
17.2.2 Multi-Manifold Based Classification
344(1)
17.2.3 Dimensionality Selection
345(2)
17.2.4 The Procedures
347(2)
17.3 Experiments and Discussion
349(8)
17.3.1 Data Sets
349(1)
17.3.2 Feature Extraction
350(2)
17.3.3 Experimental Results
352(3)
17.3.4 Discussion
355(2)
17.4 Summary
357(1)
References
357(4)
Chapter 18 Cross Domain Facial Expression Recognition
361(22)
18.1 Introduction
361(3)
18.2 A Transfer Learning Based Approach
364(7)
18.2.1 Problem Formulation
364(1)
18.2.2 Overview of the Approach
365(1)
18.2.3 Training with Transfer Learning
366(2)
18.2.4 Evaluation Experiments
368(3)
18.3 A Discriminative Feature Adaptation Approach
371(8)
18.3.1 Problem Formulation
372(1)
18.3.2 Domain Matching
373(1)
18.3.3 Discriminative Analysis
373(2)
18.3.4 Optimization for DFA
375(1)
18.3.5 Evaluation Experiments
376(1)
18.3.6 Results and Discussion
377(2)
18.4 Summary
379(1)
References
380(3)
Chapter 19 Book Review and Future Work
383(10)
19.1 Book Recapitulation
383(6)
19.2 Challenges and Future Work
389(4)
Index 393
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