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 MATLABAr code fragments, and 200 examples, Biometric Inverse Problems sets the standard for innovation and authority in biometric data synthesis, modeling, and analysis.Based on the authorsa#39; 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.
|Title||:||Biometric Inverse Problems|
|Author||:||Svetlana N. Yanushkevich, Adrian Stoica, Vlad P. Shmerko, Denis V. Popel|
|Publisher||:||CRC Press - 2005-05-05|