Computational Methods In Biometric Authentication
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Computational Methods in Biometric Authentication
Author: Michael E. Schuckers
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-06-03
Biometrics, the science of using physical traits to identify individuals, is playing an increasing role in our security-conscious society and across the globe. Biometric authentication, or bioauthentication, systems are being used to secure everything from amusement parks to bank accounts to military installations. Yet developments in this field have not been matched by an equivalent improvement in the statistical methods for evaluating these systems. Compensating for this need, this unique text/reference provides a basic statistical methodology for practitioners and testers of bioauthentication devices, supplying a set of rigorous statistical methods for evaluating biometric authentication systems. This framework of methods can be extended and generalized for a wide range of applications and tests. This is the first single resource on statistical methods for estimation and comparison of the performance of biometric authentication systems. The book focuses on six common performance metrics: for each metric, statistical methods are derived for a single system that incorporates confidence intervals, hypothesis tests, sample size calculations, power calculations and prediction intervals. These methods are also extended to allow for the statistical comparison and evaluation of multiple systems for both independent and paired data. Topics and features: * Provides a statistical methodology for the most common biometric performance metrics: failure to enroll (FTE), failure to acquire (FTA), false non-match rate (FNMR), false match rate (FMR), and receiver operating characteristic (ROC) curves * Presents methods for the comparison of two or more biometric performance metrics * Introduces a new bootstrap methodology for FMR and ROC curve estimation * Supplies more than 120 examples, using publicly available biometric data where possible * Discusses the addition of prediction intervals to the bioauthentication statistical toolset * Describes sample-size and power calculations for FTE, FTA, FNMR and FMR Researchers, managers and decisions makers needing to compare biometric systems across a variety of metrics will find within this reference an invaluable set of statistical tools. Written for an upper-level undergraduate or master’s level audience with a quantitative background, readers are also expected to have an understanding of the topics in a typical undergraduate statistics course. Dr. Michael E. Schuckers is Associate Professor of Statistics at St. Lawrence University, Canton, NY, and a member of the Center for Identification Technology Research.
Proceedings of the International Workshop on Computational Intelligence in Security for Information Systems CISIS 2008
Author: Emilio Corchado
language: en
Publisher: Springer Science & Business Media
Release Date: 2008-10-24
The research scenario in advanced systems for protecting critical infrastructures and for deeply networked information tools highlights a growing link between security issues and the need for intelligent processing abilities in the area of information s- tems. To face the ever-evolving nature of cyber-threats, monitoring systems must have adaptive capabilities for continuous adjustment and timely, effective response to modifications in the environment. Moreover, the risks of improper access pose the need for advanced identification methods, including protocols to enforce comput- security policies and biometry-related technologies for physical authentication. C- putational Intelligence methods offer a wide variety of approaches that can be fruitful in those areas, and can play a crucial role in the adaptive process by their ability to learn empirically and adapt a system’s behaviour accordingly. The International Workshop on Computational Intelligence for Security in Inf- mation Systems (CISIS) proposes a meeting ground to the various communities - volved in building intelligent systems for security, namely: information security, data mining, adaptive learning methods and soft computing among others. The main goal is to allow experts and researchers to assess the benefits of learning methods in the data-mining area for information-security applications. The Workshop offers the opportunity to interact with the leading industries actively involved in the critical area of security, and have a picture of the current solutions adopted in practical domains. This volume of Advances in Soft Computing contains accepted papers presented at CISIS’08, which was held in Genova, Italy, on October 23rd–24th, 2008.
Artificial Intelligence and Bioinspired Computational Methods
This book gathers the refereed proceedings of the Artificial Intelligence and Bioinspired Computational Methods Section of the 9th Computer Science On-line Conference 2020 (CSOC 2020), held on-line in April 2020. Artificial intelligence and bioinspired computational methods now represent crucial areas of computer science research. The topics presented here reflect the current discussion on cutting-edge hybrid and bioinspired algorithms and their applications.