Contents -- Preface -- Acknowledgments -- PART 1: SYNTHESIS IN BIOMETRICS -- 1. Introduction to Synthesis in Biometrics S. Yanushkevich, V. Shmerko, A. Stoica, P. Wang, S. Srihari -- Contents -- 1.1. Introduction -- 1.1.1. Basic Paradigm of Synthetic Biometric Data -- 1.2. Synthetic Approaches -- 1.2.1. Image Synthesis -- 1.2.2. Physics-Based Modeling -- 1.2.3. Modeling Taxonomy -- 1.3. Synthetic Biometrics -- 1.3.1. Synthetic Fingerprints -- 1.3.2. Synthetic Signatures -- 1.3.3. Synthetic Retina and Iris Images -- 1.3.4. Synthetic Speech and Voice -- 1.3.5. Gait Modeling -- 1.3.6. Synthetic Faces -- 1.3.6.1. Animation as Behavioral Face Synthesis -- 1.3.6.2. Caricature as Synthetic Face -- 1.3.6.3. Synthetic Emotions and Expressions -- 1.4. Examples of Usage of Synthetic Biometrics -- 1.4.1. Testing -- 1.4.2. Databases of Synthetic Biometric Information -- 1.4.3. Humanoid Robots -- 1.4.4. Cancelable Biometrics -- 1.4.5. Synthetic Biometric Data in the Development of a New Generation of Lie Detectors -- 1.4.6. Synthetic Biometric Data in Early Warning and Detection System Design -- 1.5. Biometric Data Model Validation -- 1.6. Ethical and Social Aspects of Inverse Biometrics -- 1.7. Conclusion -- Acknowledgment -- Bibliography -- 2. Signature Analysis, Veri.cation and Synthesis in Pervasive Environments Denis V. Popel -- 2.1. Introduction -- 2.2. Signature Representation -- 2.3. Signature Comparison -- 2.3.1. Sequence Alignment -- Alignment of continuous sequences -- 2.3.2. Algorithm and Experimental Results -- 2.4. Signature Synthesis -- 2.4.1. Signature Synthesis Techniques -- 2.4.2. Statistically Meaningful Synthesis -- 2.4.3. Geometrically Meaningful Synthesis -- 2.4.4. Algorithm and Experimental Results -- 2.5. System Architecture -- 2.6. Concluding Remarks and Future Work -- Acknowledgments -- Bibliography.

3. Local B-Spline Multiresolution with Example in Iris Synthesis and Volumetric Rendering Faramarz F. Samavati, Richard H. Bartels, Luke Olsen -- Contents -- 3.1. Introduction -- 3.2. Wavelets and Multiresolution Background -- 3.3. Review of Construction -- 3.4. Other B-Spline Multiresolution Filters -- 3.4.1. Short Filters for Cubic B-Spline -- 3.4.2. Cubic B-Spline Filters: Inverse Powers of Two -- 3.4.3. Short Filters for Quadratic B-Spline -- 3.4.4. Wide Filters for Quadratic B-Spline -- 3.5. Extraordinary (Boundary) Filters -- 3.5.1. Boundary Filters for Cubic B-Spline -- 3.5.1.1. Construction of A -- 3.5.1.2. B and Q -- 3.5.2. Boundary Filters for Short Cubic B-Spline -- 3.5.3. Boundary Filters for Short Quadratic B-Spline -- 3.5.4. Boundary Filters for Wide Quadratic B-Spline -- 3.6. Efficient Algorithm -- 3.7. Extensions -- 3.7.1. Periodic (Closed) Curves -- 3.7.2. Tensor Product Surfaces -- 3.7.2.1. Open-Open Surfaces -- 3.7.2.2. Open-Closed Surfaces -- 3.7.2.3. Closed-Closed Surfaces -- 3.7.3. 2D Images -- 3.7.3.1. Symmetric Extension -- 3.7.4. 3D Images -- 3.8. Results, Examples and Applications -- 3.8.1. Example Applications -- 3.8.2. Iris Synthesis -- 3.8.3. Real-Time Contextual Close-up of Volumetric Data -- 3.9. Conclusion -- Acknowledgement -- Bibliography -- 4. Computational Geometry and Image Processing in Biometrics: On the Path to Convergence Marina L.Gavrilova -- Contents -- 4.1. Introduction -- 4.2. Biometric Methods -- 4.3. Chapter Overview -- 4.4. Biometric System Architecture -- 4.5. Computational Geometry Methods in Biometrics -- 4.5.1. Voronoi Diagram Techniques in Biometrics -- 4.5.1.1. Voronoi Diagram Preliminaries -- 4.5.1.2. Voronoi Diagrams in Fingerprint Matching -- 4.5.1.3. Delaunay Triangulation for Fingerprint Matching and Deformation Modeling -- 4.5.1.4. System Implementation and Experiments.

4.5.2. Distance Distribution Computation in Biometrics -- 4.5.2.1. Weighted Distance Metrics for Establishing the Biometric Threshold -- 4.5.2.2. Distance Transform Preliminaries -- 4.5.2.3. Face Modeling using Distance Transform -- 4.5.2.4. Distance Transform for Ridge Extraction -- 4.5.2.5. Pattern Matching -- 4.5.2.6. Distance Transform for Pattern Matching -- 4.5.3. Medial Axis Transform for Boundary and Skeleton Extraction and Topological Properties Identi.cation -- 4.5.3.1. Topology-Based Approximation of Image for Feature Extraction along the Boundary -- 4.5.3.2. Edge Detection for Feature Lines -- 4.5.4. Topology-Based Approach for Generation and Synthesis of New Biometric Information -- 4.6. Conclusions -- Acknowledgments -- Bibliography -- PART 2: ANALYSIS IN BIOMETRICS -- 5. A Statistical Model for Biometric Verification Sargur Srihari, Harish Srinivasan -- Contents -- 5.1. Introduction -- 5.2. Discriminating Elements and Similarity -- 5.3. Statistical Formulation -- 5.3.1. Gaussian Case -- 5.3.2. Gamma Case -- 5.3.3. Mixture Model Case -- 5.3.4. Strength of Evidence -- 5.4. Application of Model -- 5.4.1. Fingerprint Verification -- 5.4.1.1. ROC Method of Learning -- 5.4.1.2. Parametric Learning using Gamma Distributions -- 5.4.2. Writer Verification -- 5.5. Concluding Remarks -- Bibliography -- 6. Composite Systems for Handwritten Signature Recognition Jim R. Parker -- Contents -- 6.1. Introduction -- 6.2. Simple Distances Between Signatures -- 6.2.1. Simple Direct Comparison: Global Relative Distance -- 6.2.2. Temporal Distance -- 6.3. Experimental Protocol: Trial 1 -- 6.3.1. Results: Trial 1 -- 6.4. Two Other Methods - A Comparison -- 6.4.1. Slope Histograms -- 6.4.2. Shadow Masks -- 6.4.3. Intermediate Results -- 6.5. Composite Classifiers -- 6.5.1. Merging Type 1 Responses -- 6.5.2. Merging Type 2 Responses.

6.5.3. Merging Type 3 Responses -- 6.5.4. Results from the Multiple Classifier -- 6.5.5. Further Classifier Combination Techniques -- 6.5.6. Empirical Evaluation -- 6.6. Conclusions -- Acknowledgments -- Bibliography -- 7. Force Field Feature Extraction for Ear Biometrics David Hurley -- Contents -- 7.1. Introduction -- 7.2. Ear Topology -- 7.3. The Force Field Transforms -- 7.3.1. Transformation of the Image to a Force Field -- 7.3.2. The Energy Transform for an Ear Image -- 7.3.3. An Invertible Linear Transform -- 7.4. Force Field Feature Extraction -- 7.4.1. Field Line Feature Extraction -- 7.4.2. Dome Shape and Brightness Sensitivity -- 7.4.3. Convergence Feature Extraction -- 7.5. Ear Recognition -- 7.5.1. Experiment Results and Analysis -- 7.6. Conclusions -- Acknowledgments -- Bibliography -- 8. Nontensor-Product-Wavelet-Based Facial Feature Representation Xinge You, Qiuhui Chen, Patrick Wang, Dan Zhang -- Contents -- 8.1. Introduction -- 8.2. Construction of Nontensor Product Wavelet Filters Banks -- 8.2.1. Characteristics of Centrally Symmetric Orthogonal Matrix -- 8.2.2. Nontensor Product Wavelet Filter -- 8.2.3. Examples -- 8.3. Experimental Results -- 8.4. Conclusions -- Acknowledgments -- Bibliography -- 9. Palmprint Identification by Fused Wavelet Characteristics Guangming Lu, David Zhang, Wai-Kin Kong, Qingmin Liao -- Contents -- 9.1. Introduction -- 9.2. Palmprint Images Collection and Preprocessing -- 9.3. Wavelet Based Feature Extraction -- 9.4. Palmprint Feature Matching and Decision Fusion -- 9.5. Experimental Results -- 9.6. Conclusions -- Acknowledgments -- Bibliography -- 10. Behavioral Biometrics for Online Computer User Monitoring Ahmed Awad E. Ahmed, Issa Traoré -- Contents -- 10.1. Introduction -- 10.2. Biometrics Modes and Metrics -- 10.3. Mouse dynamics -- 10.3.1. Overview -- 10.3.2. Detection Process.

10.3.3. Silence Analysis -- 10.4. Keystroke Dynamics -- 10.4.1. Overview -- 10.4.2. Free Text Detection Using Approximation Matrix Technique -- 10.4.3. Free Text Detection Based on Keyboard Layout Mapping -- 10.4.4. Free Text detection Based on Sorted Time Mapping -- 10.5. Conclusion -- Acknowledgments -- Bibliography -- PART 3: BIOMETRIC SYSTEMS AND APPLICATIONS -- 11. Large-Scale Biometric Identi.cation: Challenges and Solutions Nalini K. Ratha, Ruud M. Bolle, Sharath Pankant -- Contents -- 11.1. Introduction -- 11.2. Biometrics Identification versus Verification -- 11.3. Terminology -- 11.4. Positive Identi.cation and Verification -- 11.5. Negative Identi.cation and Screening -- 11.6. Biometric Specific Errors -- 11.7. Additional Terminology -- 11.8. Identi.cation Methods -- 11.9. The Closed-World Assumption -- 11.10. Performance Evaluation -- 11.10.1. Simple FAR(M) and FRR(M) -- 11.10.2. Reliability and Selectivity -- 11.10.3. Cumulative Match Curve (CMC) -- 11.10.4. Recall-Precision Curve -- 11.11. Fingerprint Recognition -- 11.12. Conclusions -- Bibliography -- 12. Evolutionary Algorithms: Basic Concepts and Applications in Biometrics Carlos A. Coello Coello -- Contents -- 12.1. Introduction -- 12.2. Basic Notions of Evolutionary Algorithms -- 12.2.1. Evolution Strategies -- 12.2.2. Evolutionary Programming -- 12.2.3. Genetic Algorithms -- 12.2.4. Genetic Programming -- 12.3. A More General View of Evolutionary Algorithms -- 12.4. Some Applications in Biometrics -- 12.4.1. Fingerprint Compression -- 12.4.2. Facial Modeling -- 12.4.3. Hand Image Classification -- 12.4.4. Handwritten Character Recognition -- 12.4.5. Keystroke Dynamics Identity Verification -- 12.4.6. Voice Identification -- 12.5. Conclusions -- Acknowledgments -- Bibliography -- 13. Some Concerns on the Measurement for Biometric Analysis and Applications Patrick S. P. Wang.

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