Biometric Inverse Problems

Svetlana N. Yanushkevich, Adrian Stoica, Vlad P. Shmerko, Denis V. Popel

May 5, 2005 by CRC Press
Reference - 416 Pages - 75 B/W Illustrations
ISBN 9780849328992 - CAT# 2899

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  • Provides the first comprehensive introduction to solutions to inverse problems in biometrics, leading up to state-of-the-art mathematical techniques and challenges
  • Offers new methods, algorithms, and recommendations for approaching biometrics from the inverse perspective
  • Presents a brief overview of the main techniques of both direct and inverse biometrics, such as statistical methods, data storage, image processing, pattern recognition, and more
  • Includes ample case studies to illustrate practical applications, such as facial modeling for improved lie detectors and robotics, and numerous references for more in-depth information on each topic
  • Contains abundant figures, examples, problems, codes, and algorithms for ready implementation
  • Summary

    Traditional methods of biometric analysis are unable to overcome the limitations of existing approaches, mainly due to the lack of standards for input data, privacy concerns involving use and storage of actual biometric data, and unacceptable accuracy. Exploring solutions to inverse problems in biometrics transcends such limits and allows rich analysis of biometric information and systems for improved performance and testing. Although some particular inverse problems appear in the literature, until now there has been no comprehensive reference for these problems.

    Biometric Inverse Problems provides the first comprehensive treatment of biometric data synthesis and modeling. This groundbreaking reference comprises eight self-contained chapters that cover the principles of biometric inverse problems; basics of data structure design; new automatic synthetic signature, fingerprint, and iris design; synthetic faces and DNA; and new tools for biometrics based on Voronoi diagrams. Based on the authors' vast experience in the field, the book authoritatively examines new approaches and methodologies in both direct and inverse biometrics, providing invaluable analytical and benchmarking tools. The authors include case studies, examples, and implementation codes for practical illustration of the methods.

    Loaded with approximately 200 figures, 60 problems, 50 MATLAB® code fragments, and 200 examples, Biometric Inverse Problems sets the standard for innovation and authority in biometric data synthesis, modeling, and analysis.


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