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Imaging for Forensics and Security: From Theory to Practice 2009 ed. [Hardback]

  • Formāts: Hardback, 212 pages, height x width: 235x155 mm, weight: 505 g, 60 Illustrations, black and white; XVIII, 212 p. 60 illus., 1 Hardback
  • Sērija : Signals and Communication Technology
  • Izdošanas datums: 30-Jul-2009
  • Izdevniecība: Springer-Verlag New York Inc.
  • ISBN-10: 0387095314
  • ISBN-13: 9780387095318
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Imaging for Forensics and Security: From Theory to Practice 2009 ed.Palielināt
  • Formāts: Hardback, 212 pages, height x width: 235x155 mm, weight: 505 g, 60 Illustrations, black and white; XVIII, 212 p. 60 illus., 1 Hardback
  • Sērija : Signals and Communication Technology
  • Izdošanas datums: 30-Jul-2009
  • Izdevniecība: Springer-Verlag New York Inc.
  • ISBN-10: 0387095314
  • ISBN-13: 9780387095318
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780387095318.html
Keywords:
Imaging for Forensics and Security: From Theory to Practice provides a detailed analysis of new imaging and pattern recognition techniques for the understanding and deployment of biometrics and forensic techniques as practical solutions to increase security. It contains a collection of the recent advances in the technology ranging from theory, design, and implementation to performance evaluation of biometric and forensic systems. This book also contains new methods such as the multiscale approach, directional filter bank, and wavelet maxima for the development of practical solutions to biometric problems.



The book introduces a new forensic system based on shoeprint imagery with advanced techniques for use in forensics applications. It also presents the concept of protecting the originality of biometric images stored in databases against intentional and unintentional attacks and fraud detection data in order to further increase the security.
Introduction and Preliminaries on Biometrics and Forensics Systems
1(10)
Introduction
1(1)
Definition of Biometrics
1(4)
Biometric Characteristics
2(1)
Biometric Modalities
2(3)
Recognition/Verification/Watch-List
5(1)
Verification: Am I Who I Claim to Be?
5(1)
Recognition: Who Am I?
5(1)
The Watch-List: Are You Looking for Me?
6(1)
Steps of a Typical Biometric Recognition Application
6(3)
Biometric Data Localisation
6(1)
Normalisation and Pre-processing
7(1)
Feature Extraction
8(1)
Matching
9(1)
Databases
9(1)
Summary
9(1)
References
10(1)
Data Representation and Analysis
11(10)
Introduction
11(1)
Data Acquisition
12(3)
Sensor Module
13(1)
Data Storage
14(1)
Feature Extraction
15(1)
Matcher
16(1)
System Testing
17(1)
Performance Evaluation
17(1)
Conclusion
18(1)
References
19(2)
Improving Face Recognition Using Directional Faces
21(28)
Introduction
21(1)
Face Recognition Basics
22(4)
Recognition/verification
22(1)
Steps of a Typical Face Recognition Application
23(3)
Previous Work
26(5)
Principal Component Analysis (PCA)
26(1)
Independent Component Analysis (ICA)
27(1)
Linear Discriminant Analysis (LDA)
28(1)
Subspace Discriminant Analysis (SDA)
29(2)
Face Recognition Using Filter Banks
31(6)
Gabor Filter Bank
31(2)
Directional Filter Bank: A Review
33(4)
Proposed Method and Results Analysis
37(8)
Proposed Method
37(1)
PCA
38(1)
ICA
39(2)
LDA
41(1)
SDA
41(2)
FERET Database Results
43(2)
Conclusion
45(1)
References
45(4)
Recent Advances in Iris Recognition: A Multiscale Approach
49(30)
Introduction
49(2)
Related Work: A Review
51(1)
Iris Localisation
52(3)
Background
52(1)
Iris Segmentation
52(1)
Existing Methods for Iris Localisation
53(2)
Proposed Method for Iris Localisation
55(12)
Motivation
55(2)
The Multiscale Method
57(8)
Results and Analysis
65(2)
Texture Analysis and Feature Extraction
67(4)
Wavelet Maxima Components
68(1)
Special Gabor Filter Bank
68(2)
Proposed Method
70(1)
Matching
71(1)
Experimental Results and Analysis
72(2)
Database
72(1)
Combined Multiresolution Feature Extraction Techniques
72(1)
Template Computation
73(1)
Comparison with Existing Methods
73(1)
Discussion and Future Work
74(1)
Conclusion
75(1)
References
75(4)
Spread Transform Watermarking Using Complex Wavelets
79(38)
Introduction
79(1)
Wavelet Transforms
80(6)
Dual Tree Complex Wavelet Transform
80(3)
Non-redundant Complex Wavelet Transform
83(3)
Visual Models
86(8)
Chou's Model
87(6)
Loo's Model
93(1)
Hybrid Model
94(1)
Watermarking as Communication with Side Information
94(4)
Quantisation Index Modulation
96(1)
Spread Transform Watermarking
97(1)
Proposed Algorithm
98(2)
Encoding of Watermark
99(1)
Decoding of Watermark
100(1)
Information Theoretic Analysis
100(13)
Decoding of Watermark
101(1)
Parallel Gaussian Channels
102(3)
Watermarking Game
105(5)
Non-iid Data
110(1)
Fixed Embedding Strategies
111(2)
Conclusion
113(1)
References
113(4)
Protection of Fingerprint Data Using Watermarking
117(26)
Introduction
117(2)
Generic Watermarking System
119(4)
State-of-the-Art
123(1)
Optimum Watermark Detection
124(3)
Statistical Data Modelling and Application to Watermark Detection
127(3)
Laplacian and Generalised Gaussian Models
128(1)
Alpha Stable Model
129(1)
Experimental Results
130(8)
Experimental Modelling of DWT Coefficients
132(3)
Experimental Watermarking Results
135(3)
Conclusions
138(1)
References
139(4)
Shoemark Recognition for Forensic Science: An Emerging Technology
143(22)
Background to the Problem of Shoemark Forensic Evidence
143(6)
Applications of a Shoemark in Forensic Science
144(2)
The Need for Automating Shoemark Classification
146(1)
Inconsistent Classification
147(1)
Importable Classification Schema
148(1)
Shoemark Processing Time Restrictions
149(1)
Collection of Shoemarks at Crime Scenes
149(8)
Shoemark Collection Procedures
150(1)
Transfer/Contact Shoemarks
150(1)
Photography of Shoemarks
151(1)
Making Casts of Shoemarks
152(1)
Gelatine Lifting of Shoemarks
153(1)
Electrostatic Lifting of Shoemarks
153(1)
Recovery of Shoemarks from Snow
154(1)
Recovery of Shoemarks using Perfect Shoemark Scan
154(1)
Making a Cast of a Shoemark Directly from a Suspect's Shoe
155(1)
Processing of Shoemarks
155(2)
Entering Data into a Computerised System
157(1)
Typical Methods for Shoemark Recognition
157(3)
Feature-Based Classification
158(1)
Classification Based on Accidental Characteristics
159(1)
Review of Shoemark Classfication Systems
160(3)
SHOE-FIT
160(1)
SHOE©
160(1)
Alexandre's System
161(1)
REBEZO
161(1)
Treadmark™
162(1)
Sicar
162(1)
SmART
162(1)
De Chazal's System
163(1)
Zhang's System
163(1)
References
163(2)
Techniques for Automatic Shoeprint Classification
165(16)
Current Approaches
165(1)
Using Phase-Only Correlation
166(6)
The POC Function
166(2)
Translation and Brightness Properties of the POC Function
168(1)
The Proposed Phase-Based Method
168(2)
Experimental Results
170(2)
Deployment of ACFs
172(7)
Shoeprint Classification Using ACFs
173(2)
Matching Metrics
175(1)
Optimum Trade-Off Synthetic Discriminant Function Filter
176(1)
Unconstrained OTSDF Filter
177(1)
Tests and Results
178(1)
Conclusion
179(1)
References
180(1)
Automatic Shoeprint Image Retrieval Using Local Features
181(22)
Motivations
181(1)
Local Image Features
181(8)
New Local Feature Detector: Modified Harris-Laplace Detector
182(4)
Local Feature Descriptors
186(2)
Similarity Measure
188(1)
Experimental Results
189(10)
Shoeprint Image Databases
189(10)
Summary
199(1)
References
200(3)
Index 203