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3D Face Modeling, Analysis and Recognition [Kõva köide]

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(Florida State University), (Universiteit Utrecht), (Telecom Lille 1)
  • Formaat: Hardback, 224 pages, kõrgus x laius x paksus: 252x177x17 mm, kaal: 603 g
  • Ilmumisaeg: 09-Aug-2013
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0470666412
  • ISBN-13: 9780470666418
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3D Face Modeling, Analysis and RecognitionSuurem pilt
  • Formaat: Hardback, 224 pages, kõrgus x laius x paksus: 252x177x17 mm, kaal: 603 g
  • Ilmumisaeg: 09-Aug-2013
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0470666412
  • ISBN-13: 9780470666418
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780470666418.html
Märksõnad:
3D Face Modeling, Analysis and Recognition presents methodologies for analyzing shapes of facial surfaces, develops computational tools for analyzing 3D face data, and illustrates them using state-of-the-art applications. The methodologies chosen are based on efficient representations, metrics, comparisons, and classifications of features that are especially relevant in the context of 3D measurements of human faces. These frameworks have a long-term utility in face analysis, taking into account the anticipated improvements in data collection, data storage, processing speeds, and application scenarios expected as the discipline develops further.

The book covers face acquisition through 3D scanners and 3D face pre-processing, before examining the three main approaches for 3D facial surface analysis and recognition: facial curves; facial surface features; and 3D morphable models. Whilst the focus of these chapters is fundamentals and methodologies, the algorithms provided are tested on facial biometric data, thereby continually showing how the methods can be applied.

Key features: Explores the underlying mathematics and will apply these mathematical techniques to 3D face analysis and recognition Provides coverage of a wide range of applications including biometrics, forensic applications, facial expression analysis, and model fitting to 2D images Contains numerous exercises and algorithms throughout the book
Preface ix
List of Contributors
xiii
1 3D Face Modeling
1(38)
Boulbaba Ben Amor
Mohsen Ardabilian
Liming Chen
1.1 Challenges and Taxonomy of Techniques
2(1)
1.2 Background
3(4)
1.2.1 Depth from Triangulation
4(1)
1.2.2 Shape from Shading
5(1)
1.2.3 Depth from Time of Flight (ToF)
6(1)
1.3 Static 3D Face Modeling
7(7)
1.3.1 Laser-stripe Scanning
7(1)
1.3.2 Time-coded Structured Light
8(3)
1.3.3 Multiview Static Reconstruction
11(3)
1.4 Dynamic 3D Face Reconstruction
14(17)
1.4.1 Multiview Dynamic Reconstruction
14(3)
1.4.2 Photometric Stereo
17(1)
1.4.3 Structured Light
18(6)
1.4.4 Spacetime Faces
24
1.4.5 Template-based Post-processing
21(10)
1.5 Summary and Conclusions
31(8)
Exercises
33(2)
References
35(4)
2 3D Face Surface Analysis and Recognition Based on Facial Surface Features
39(38)
Faisal Radhi
M. Al-Osaimi
Mohammed Bennamoun
2.1 Geometry of 3D Facial Surface
39(14)
2.1.1 Primary 3D Surface Representations
40(7)
2.7.2 Rigid 3D Transformations
47(2)
2.1.3 Decimation of 3D Surfaces
49(2)
2.1.4 Geometric and Topological Aspects of the Human Face
51(2)
2.2 Curvatures Extraction from 3D Face Surface
53(4)
2.2.7 Theoretical Concepts on 3D Curvatures
53(3)
2.2.2 Practical Curvature Extraction Methods
56(1)
2.3 3D Face Segmentation
57(2)
2.3.1 Curvature-based 3D Face Segmentation
57(1)
2.3.2 Bilateral Profile-based 3D Face Segmentation
58(1)
2.4 3D Face Surface Feature Extraction and Matching
59(12)
2.4.1 Holistic 3D Facial Features
60(7)
2.4.2 Regional 3D Facial Features
67(1)
2.4.3 Point 3D Facial Features
68(3)
2.5 Deformation Modeling of 3D Face Surface
71(6)
Exercises
73(1)
References
74(3)
3 3D Face Surface Analysis and Recognition Based on Facial Curves
77(42)
Hassen Drira
Stefano Berretti
Boulbaba Ben Amor
Mohamed Daoudi
Anuj Srivastava
Alberto del Bimbo
Pietro Pala
3.1 Introduction
77(1)
3.2 Facial Surface Modeling
78(2)
3.3 Parametric Representation of Curves
80(1)
3.4 Facial Shape Representation Using Radial Curves
81(1)
3.5 Shape Space of Open Curves
81(9)
3.5.1 Shape Representation
82(2)
3.5.2 Geometry of Preshape Space
84(2)
3.5.3 Reparametrization Estimation by Using Dynamic Programming
86(2)
3.5.4 Extension to Facial Surfaces Shape Analysis
88(2)
3.6 The Dense Scalar Field (DSF)
90(4)
3.7 Statistical Shape Analysis
94(4)
3.7.1 Statistics on Manifolds: Karcher Mean
94(2)
3.7.2 Learning Statistical Models in Shape Space
96(2)
3.8 Applications of Statistical Shape Analysis
98(5)
3.8.1 3D Face Restoration
98(3)
3.8.2 Hierarchical Organization of Facial Shapes
101(2)
3.9 The Iso-geodesic Stripes
103(16)
3.9.1 Extraction of Facial Stripes
107(2)
3.9.2 Computing Relationships between Facial Stripes
109(4)
3.9.3 Face Representation and Matching Using Iso-geodesic Stripes
113(1)
Exercises
114(2)
Glossary
116(1)
References
117(2)
4 3D Morphable Models for Face Surface Analysis and Recognition
119(30)
Frank B. ter Haar
Remco Veltkamp
4.1 Introduction
120(1)
4.2 Data Sets
121(1)
4.3 Face Model Fitting
122(7)
4.3.1 Distance Measure
122(1)
4.3.2 Iterative Face Fitting
123(1)
4.3.3 Coarse Fitting
124(1)
4.3.4 Fine Fitting
124(1)
4.3.5 Multiple Components
125(1)
4.3.6 Results
126(3)
4.4 Dynamic Model Expansion
129(12)
4.4.1 Bootstrapping Algorithm
131(5)
4.4.2 Results
136(5)
4.5 Face Matching
141(3)
4.5.1 Comparison
141(1)
4.5.2 Results
142(2)
4.6 Concluding Remarks
144(5)
Exercises
145(1)
References
146(3)
5 Applications
149(54)
Stefano Berretti
Boulbaba Ben Amor
Hassen Drira
Mohamed Daoudi
Anuj Srivastava
Alberto del Bimbo
Pietro Pala
5.1 Introduction
149(1)
5.2 3D Face Databases
150(7)
5.3 3D Face Recognition
157(13)
5.5.7 Challenges of 3D Face Recognition
158(1)
5.3.2 3D Face Recognition: State of the Art
159(3)
5.3.3 Partial Face Matching
162(6)
5.3.4 Comparison of State-of-the-Art Methods
168(2)
5.4 Facial Expression Analysis
170(14)
5.4.1 3D Facial Expression Recognition: State of the Art
171(2)
5.4.2 Semi-automatic 3D Facial Expression Recognition
173(7)
5.4.3 Fully Automatic 3D Facial Expression Recognition
180(4)
5.5 4D Facial Expression Recognition
184(19)
5.5.1 The BU-4DFE Database
186(1)
5.5.2 3D Shape Motion Analysis
187(5)
5.5.3 Discussion and Comparative Evaluation
192(1)
Exercises
192(1)
Glossary
193(5)
References
198(5)
Index 203
Mohamed Daoudi, TELECOM Lille 1, France Professor Daoudi is a member of the computer science department at TELECOM Lille 1, and a member of the IEEE. Prof. Daoudi is an editor of the Journal of Multimedia and has been a guest co-editor of the Annals of Telecommunications for a special issue on Technologies and Tools for 3D Imaging. He co-edited 3D Object Processing: Compression, Indexing and Watermarking published by Wiley in 2008.

Anuj Srivastava, Florida State University, USA Professor Srivastava is a member of the department of statistics at Florida State University, and a member of the IEEE and ASA. He has been an associate editor of the Journal of Statistical Planning and Interference, IEEE Transactions on Signal Processing, and IEEE Transactions on Pattern Analysis and Machine Intelligence, which he also edited a special issue of on Shape Modeling. He has published over 30 journal papers and 7 book chapters in edited volumes.

Remco Veltkamp, Universiteit Utrecht, The Netherlands Professor Veltkamp is a member of the department of Information and Computing Sciences at Utrecht University, focusing on multimedia applications. He is an editor of Pattern Recognition Journal and the International Journal on Shape Modeling. He has also guest edited several journals including a special issue on Multimedia Algorithmics in  Multimedia Tools and Applications, and a special issue on Shape Reasoning and Understanding in Computers & Graphics. Prof. Veltkamp has published 30 journal papers, 13 book chapters in edited volumes, co-edited several conference proceedings and has co-edited State-of-the-art in Content-based Image and Video Retrieval published by Springer in 2001.