Cover image for 3D face modeling, analysis and recognition
Title:
3D face modeling, analysis and recognition
Publication Information:
ENK, : Wiley, 2013.
Physical Description:
xi, 205 p. : ill. (some col.) ; 26 cm.
ISBN:
9780470666418

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30000010322017 TA1637 D363 2013 Open Access Book Book
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Summary

Summary

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


Author Notes

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.


Table of Contents

Boulbaba Ben Amor and Mohsen Ardabilian and Liming ChenFaisal Radhi M. Al-Osaimi and Mohammed BennamounHassen Drira and Stefano Berretti and Boulbaba Ben Amor and Mohamed Daoudi and Anuj Srivastava and Alberto del Bimbo and Pietro PalaFrank B. ter Haar and Remco VeltkampStefano Berretti and Boulbaba Ben Amor and Hassen Drira and Mohamed Daoudi and Anuj Srivastava and Alberto del Bimbo and Pietro Pala
Prefacep. ix
List of Contributorsp. xiii
1 3D Face Modelingp. 1
1.1 Challenges and Taxonomy of Techniquesp. 2
1.2 Backgroundp. 3
1.2.1 Depth from Triangulationp. 4
1.2.2 Shape from Shadingp. 5
1.2.3 Depth from Time of Flight (ToF)p. 6
1.3 Static 3D Face Modelingp. 7
1.3.1 Laser-stripe Scanningp. 7
1.3.2 Time-coded Structured Lightp. 8
1.3.3 Multiview Static Reconstructionp. 11
1.4 Dynamic 3D Face Reconstructionp. 14
1.4.1 Multiview Dynamic Reconstructionp. 14
1.4.2 Photometric Stereop. 17
1.4.3 Structured Lightp. 18
1.4.4 Spacetime Facesp. 24
1.4.5 Template-based Post-processingp. 27
1.5 Summary and Conclusionsp. 31
Exercisesp. 33
Referencesp. 35
2 3D Face Surface Analysis and Recognition Based on Facial Surface Featuresp. 39
2.1 Geometry of 3D Facial Surfacep. 39
2.1.1 Primary 3D Surface Representationsp. 40
2.1.2 Rigid 3D Transformationsp. 47
2.1.3 Decimation of 3D Surfacesp. 49
2.1.4 Geometric and Topological Aspects of the Human Facep. 51
2.2 Curvatures Extraction from 3D Face Surfacep. 53
2.2.1 Theoretical Concepts on 3D Curvaturesp. 53
2.2.2 Practical Curvature Extraction Methodsp. 56
2.3 3D Face Segmentationp. 57
2.3.1 Curvature-based 3D Face Segmentationp. 57
2.3.2 Bilateral Profile-based 3D Face Segmentationp. 58
2.4 3D Face Surface Feature Extraction and Matchingp. 59
2.4.1 Holistic 3D Facial Featuresp. 60
2.4.2 Regional 3D Facial Featuresp. 67
2.4.3 Point 3D Facial Featuresp. 68
2.5 Deformation Modeling of 3D Face Surfacep. 71
Exercisesp. 73
Referencesp. 74
3 3D Face Surface Analysis and Recognition Based on Facial Curvesp. 77
3.1 Introductionp. 77
3.2 Facial Surface Modelingp. 78
3.3 Parametric Representation of Curvesp. 80
3.4 Facial Shape Representation Using Radial Curvesp. 81
3.5 Shape Space of Open Curvesp. 81
3.5.1 Shape Representationp. 82
3.5.2 Geometry of Preshape Spacep. 84
3.5.3 Reparametrization Estimation by Using Dynamic Programmingp. 86
3.5.4 Extension to Facial Surfaces Shape Analysisp. 88
3.6 The Dense Scalar Field (DSF)p. 90
3.7 Statistical Shape Analysisp. 94
3.7.1 Statistics on Manifolds: Karcher Meanp. 94
3.7.2 Learning Statistical Models in Shape Spacep. 96
3.8 Applications of Statistical Shape Analysisp. 98
3.8.1 3D Face Restorationp. 98
3.8.2 Hierarchical Organization of Facial Shapesp. 101
3.9 The Iso-geodesic Stripesp. 103
3.9.1 Extraction of Facial Stripesp. 107
3.9.2 Computing Relationships between Facial Stripesp. 109
3.9.3 Face Representation and Matching Using Iso-geodesic Stripesp. 113
Exercisesp. 114
Glossaryp. 116
Referencesp. 117
4 3D Morphable Models for Face Surface Analysis and Recognitionp. 119
4.1 Introductionp. 120
4.2 Data Setsp. 121
4.3 Face Model Fittingp. 122
4.3.1 Distance Measurep. 122
4.3.2 Iterative Face Fittingp. 123
4.3.3 Coarse Fittingp. 124
4.3.4 Fine Fittingp. 124
4.3.5 Multiple Componentsp. 125
4.3.6 Resultsp. 126
4.4 Dynamic Model Expansionp. 129
4.4.1 Bootstrapping Algorithmp. 131
4.4.2 Resultsp. 136
4.5 Face Matchingp. 141
4.5.1 Comparisonp. 141
4.5.2 Resultsp. 142
4.6 Concluding Remarksp. 144
Exercisesp. 145
Referencesp. 146
5 Applicationsp. 149
5.1 Introductionp. 149
5.2 3D Face Databasesp. 150
5.3 3D Face Recognitionp. 157
5.3.1 Challenges of 3D Face Recognitionp. 158
5.3.2 3D Face Recognition: State of the Aitp. 159
5.3.3 Partial Face Matchingp. 162
5.3.4 Comparison of State-of-the-Art Methodsp. 168
5.4 Facial Expression Analysisp. 170
5.4.1 3D Facial Expression Recognition: State of the Artp. 171
5.4.2 Semi-automatic 3D Facial Expression Recognitionp. 173
5.4.3 Fully Automatic 3D Facial Expression Recognitionp. 180
5.5 4D Facial Expression Recognitionp. 184
5.5.1 The BU-4DFE Databasep. 186
5.5.2 3D Shape Motion Analysisp. 187
5.5.3 Discussion and Comparative Evaluationp. 192
Exercisesp. 192
Glossaryp. 193
Referencesp. 198
Indexp. 203