Biometric System and Data Analysis: Design, Evaluation, and Data Mining

✍ Scribed by Ted Dunstone, Neil Yager (eds.)

Publisher: Springer US
Year: 2009
Tongue: English
Leaves: 282
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Biometric System and Data Analysis: Design, Evaluation, and Data Mining brings together aspects of statistics and machine learning to provide a comprehensive guide to evaluate, interpret and understand biometric data. This professional book naturally leads to topics including data mining and prediction, widely applied to other fields but not rigorously to biometrics.

This volume places an emphasis on the various performance measures available for biometric systems, what they mean, and when they should and should not be applied. The evaluation techniques are presented rigorously, however are always accompanied by intuitive explanations that convey the essence of the statistical concepts in a general manner.

Designed for a professional audience composed of practitioners and researchers in industry, Biometric System and Data Analysis: Design, Evaluation, and Data Mining is also suitable as a reference for advanced-level students in computer science and engineering.

✦ Table of Contents

Front Matter....Pages i-xx
Front Matter....Pages 1-2
An Introduction to Biometric Data Analysis....Pages 3-26
Biometric Matching Basics....Pages 27-43
Biometric Data....Pages 45-69
Multimodal Systems....Pages 71-80
Performance Testing and Reporting....Pages 81-97
Definitions....Pages 99-108
Front Matter....Pages 109-110
System Evaluation: The Statistical Basis of Biometric Systems....Pages 111-152
Individual Evaluation: The Biometric Menagerie....Pages 153-180
Group Evaluation: Data Mining for Biometrics....Pages 181-201
Front Matter....Pages 203-204
Proof of Identity....Pages 205-221
Covert Surveillance Systems....Pages 223-246
Vulnerabilities....Pages 247-262
Back Matter....Pages 263-267

✦ Subjects

Biometrics; Computer Imaging, Vision, Pattern Recognition and Graphics; Data Mining and Knowledge Discovery; Artificial Intelligence (incl. Robotics); Data Encryption; Database Management

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