TY  - BOOK
AU  - Dunstone,Ted
AU  - Yager,Neil
ED  - SpringerLink (Online service)
TI  - Biometric System and Data Analysis: Design, Evaluation, and Data Mining 
SN  - 9780387776279
AV  - QH323.5 
U1  - 570.15195 23
PY  - 2009///
CY  - Boston, MA
PB  - Springer US
KW  - Computer science
KW  - Data encryption (Computer science)
KW  - Database management
KW  - Data mining
KW  - Artificial intelligence
KW  - Computer vision
KW  - Biometrics
KW  - Computer Science
KW  - Computer Imaging, Vision, Pattern Recognition and Graphics
KW  - Data Mining and Knowledge Discovery
KW  - Artificial Intelligence (incl. Robotics)
KW  - Data Encryption
KW  - Database Management
N1  - An Overview of Biometrics -- An Introduction to Biometric Data Analysis -- Biometric Matching Basics -- Biometric Data -- Multimodal Systems -- Performance Testing and Reporting -- Definitions -- The Biometric Performance Hierarchy -- System Evaluation: The Statistical Basis of Biometric Systems -- Individual Evaluation: The Biometric Menagerie -- Group Evaluation: Data Mining for Biometrics -- Special Topics in Biometric Data Analysis -- Proof of Identity -- Covert Surveillance Systems -- Vulnerabilities
N2  - 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.
UR  - http://dx.doi.org/10.1007/978-0-387-77627-9
ER  -