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Author Yanushkevich, Svetlana N
Title Image Pattern Recognition : Synthesis and Analysis in Biometrics
Imprint Singapore : World Scientific Publishing Co Pte Ltd, 2007
©2007
book jacket
Descript 1 online resource (453 pages)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Series Series in Machine Perception and Artificial Intelligence Ser. ; v.67
Series in Machine Perception and Artificial Intelligence Ser
Note Intro -- 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 Traoŕe -- 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
Contents
Key Features:Provides the first comprehensive treatment of the relationship between biometric data analysis (direct task) and synthesis (inverse task)Authoritatively examines new approaches and methodologies in both direct and inverse biometricsIncludes ample case studies to illustrate practical applications, such as iris and facial modeling for biometric-based training systemsConsulted by Mark S Nixon of the University of South Hampton, one of the first experts in the UK on biometric authentication
Description based on publisher supplied metadata and other sources
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2020. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
Link Print version: Yanushkevich, Svetlana N Image Pattern Recognition : Synthesis and Analysis in Biometrics Singapore : World Scientific Publishing Co Pte Ltd,c2007 9789812569080
Subject Pattern recognition systems -- Congresses.;Pattern recognition systems -- Mathematical models -- Congresses.;Biometric identification -- Congresses.;Identification -- Automation -- Congresses.;Artificial intelligence -- Congresses
Electronic books
Alt Author Nixon, Mark S
Srihari, Sargur N
Wang, Patrick S. P
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