LEADER 05395nam 2200637Ia 450 001 9910141725603321 005 20200520144314.0 010 $a1-118-59263-8 010 $a1-118-59265-4 010 $a1-118-59264-6 035 $a(CKB)2670000000359259 035 $a(EBL)1204915 035 $a(OCoLC)850164908 035 $a(MiAaPQ)EBC1204915 035 $a(DLC) 2013017194 035 $a(Au-PeEL)EBL1204915 035 $a(CaPaEBR)ebr10716636 035 $a(CaONFJC)MIL494660 035 $a(EXLCZ)992670000000359259 100 $a20130423d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$a3D face modeling, analysis, and recognition$b[electronic resource] /$fMohamed Daoudi, Anuj Srivastava, Remco Veltkamp 210 $aSingapore $cWiley$d2013 215 $a1 online resource (221 p.) 300 $aDescription based upon print version of record. 311 $a0-470-66641-2 320 $aIncludes bibliographical references and index. 327 $a3D FACE MODELING, ANALYSIS AND RECOGNITION; Contents; Preface; List of Contributors; 1 3D Face Modeling; 1.1 Challenges and Taxonomy of Techniques; 1.2 Background; 1.2.1 Depth from Triangulation; 1.2.2 Shape from Shading; 1.2.3 Depth from Time of Flight (ToF); 1.3 Static 3D Face Modeling; 1.3.1 Laser-stripe Scanning; 1.3.2 Time-coded Structured Light; 1.3.3 Multiview Static Reconstruction; 1.4 Dynamic 3D Face Reconstruction; 1.4.1 Multiview Dynamic Reconstruction; 1.4.2 Photometric Stereo; 1.4.3 Structured Light; 1.4.4 Spacetime Faces; 1.4.5 Template-based Post-processing 327 $a1.5 Summary and ConclusionsExercises; References; 2 3D Face Surface Analysis and Recognition Based on Facial Surface Features; 2.1 Geometry of 3D Facial Surface; 2.1.1 Primary 3D Surface Representations; 2.1.2 Rigid 3D Transformations; 2.1.3 Decimation of 3D Surfaces; 2.1.4 Geometric and Topological Aspects of the Human Face; 2.2 Curvatures Extraction from 3D Face Surface; 2.2.1 Theoretical Concepts on 3D Curvatures; 2.2.2 Practical Curvature Extraction Methods; 2.3 3D Face Segmentation; 2.3.1 Curvature-based 3D Face Segmentation; 2.3.2 Bilateral Profile-based 3D Face Segmentation 327 $a2.4 3D Face Surface Feature Extraction and Matching2.4.1 Holistic 3D Facial Features; 2.4.2 Regional 3D Facial Features; 2.4.3 Point 3D Facial Features; 2.5 Deformation Modeling of 3D Face Surface; Exercises; References; 3 3D Face Surface Analysis and Recognition Based on Facial Curves; 3.1 Introduction; 3.2 Facial Surface Modeling; 3.3 Parametric Representation of Curves; 3.4 Facial Shape Representation Using Radial Curves; 3.5 Shape Space of Open Curves; 3.5.1 Shape Representation; 3.5.2 Geometry of Preshape Space; 3.5.3 Reparametrization Estimation by Using Dynamic Programming 327 $a3.5.4 Extension to Facial Surfaces Shape Analysis3.6 The Dense Scalar Field (DSF); 3.7 Statistical Shape Analysis; 3.7.1 Statistics on Manifolds: Karcher Mean; 3.7.2 Learning Statistical Models in Shape Space; 3.8 Applications of Statistical Shape Analysis; 3.8.1 3D Face Restoration; 3.8.2 Hierarchical Organization of Facial Shapes; 3.9 The Iso-geodesic Stripes; 3.9.1 Extraction of Facial Stripes; 3.9.2 Computing Relationships between Facial Stripes; 3.9.3 Face Representation and Matching Using Iso-geodesic Stripes; Exercises; Glossary; References 327 $a4 3D Morphable Models for Face Surface Analysis and Recognition4.1 Introduction; 4.2 Data Sets; 4.3 Face Model Fitting; 4.3.1 Distance Measure; 4.3.2 Iterative Face Fitting; 4.3.3 Coarse Fitting; 4.3.4 Fine Fitting; 4.3.5 Multiple Components; 4.3.6 Results; 4.4 Dynamic Model Expansion; 4.4.1 Bootstrapping Algorithm; 4.4.2 Results; 4.5 Face Matching; 4.5.1 Comparison; 4.5.2 Results; 4.6 Concluding Remarks; Exercises; References; 5 Applications; 5.1 Introduction; 5.2 3D Face Databases; 5.3 3D Face Recognition; 5.3.1 Challenges of 3D Face Recognition; 5.3.2 3D Face Recognition: State of the Art 327 $a5.3.3 Partial Face Matching 330 $a 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 appl 606 $aFace$xComputer simulation 606 $aHuman face recognition (Computer science) 606 $aThree-dimensional imaging 608 $aElectronic books. 615 0$aFace$xComputer simulation. 615 0$aHuman face recognition (Computer science) 615 0$aThree-dimensional imaging. 676 $a006.6/93 700 $aDaoudi$b Mohamed$f1964-$0307166 701 $aSrivastava$b Anuj$f1968-$0968703 701 $aVeltkamp$b Remco C.$f1963-$0968704 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910141725603321 996 $a3D face modeling, analysis, and recognition$92200376 997 $aUNINA