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25. Y. Dodis, L. Reyzin, and A. Smith, Fuzzy extractors and cryptography, or how to use your ngerprints, Proceedings of Eurocrypt 04, 2004, 26. E. M. Newton, L. Sweeney, and B. Malin, Preserving privacy by de-identifying face images, IEEE Trans. Knowledge Data Eng. 17:232 243, 2005. 27. Y. Zhu, S. C. Dass, and A. K. Jain, Statistical models for assessing the individuality of ngerprints, IEEE Trans. Info. Foren. Sec. 2(3, Part 1):391 401, 2007. 28. A. Adler, Vulnerabilities in biometric encryption systems, Audio- and Video-Based Biometric Person Auth. Tarrytown, NY, July 20 22, 2005. 29. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, Eigenfaces vs. Fisherfaces: Recognition using class speci c linear projection, IEEE Trans. Pattern Anal. Mach. Intell. 19:711 720, 1997. 30. W. J. Conover, Practical Nonparametric Statistics, John Wiley & Sons, New York, 1980. 31. A. Hyv rinen, Fast and robust xed-point algorithms for independent component analysis, IEEE Trans. a Neural Net. 10:626 634, 1999. 32. C. Soutar, D. Roberge, A. Stoianov, R. Gilroy, and B. Vijaya, Biometric Encryption using image processing, Proc. SPIE Int. Soc. Opt. Eng. 3314:178 188, 1998.
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This chapter presents a summary of the theoretical results and design experience obtained during the developing of a next generation of a physical access security system (PASS). The main feature of this PASS is its the ef cient support of security personnel enhanced with the situational awareness paradigm and intelligent tools. Research work was conducted at the Biometric Technologies Laboratory of the University of Calgary, Canada; and at the Humanoid Robotics Laboratory at the NASA Jet Propulsion Laboratory, California Institute of Technology. The Guidance Package: Biometrics for Airport Access Control, developed by the Assistant Secretary of Homeland Security in consultation with representatives of the aviation industry, biometric identi er industry, and the National Institute of Standards and Technology (NIST), provides criteria for the integration of biometric devices into access control systems. In this document, access control is de ned as the examination of one or more of three factors regarding an individual s
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Biometrics: Theory, Methods, and Applications. Edited by Boulgouris, Plataniotis, and Micheli-Tzanakou Copyright 2010 the Institute of Electrical and Electronics Engineers, Inc.
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identity: something they know, something they have, or something they are. This document is acknowledged by other research initiatives, including the Defense Advanced Research Projects Agency (DARPA) research program, HumanID, which is aimed at the detection, recognition, and identi cation of humans at a distance in early warning support systems for force protection and homeland defense [1]. Most existing check-point PASSes exclusively utilize the visual appearance of customers to compare against lookout checklists or suspected activity and do not effectively use the time slot before or/during access authorization, or registration to collect biometric information (body temperature, surgical changes, etc.) about individuals. Signi cant improvement of these PASSes can be achieved by using biometric devices. However, the effectiveness of known approaches to biometric-based PASS design [1 4] is limited. The reason is that biometric devices are integrated to the PASS as separate modules. The availability of a large number of biometric devices does not mean that the of cer is able to manage all of these the information streams of data captured by these devices. For example, in the advanced systems deployed in some airports, security personnel observe the customers in the monitor prior to screening, check individuals using multispectral tools, check individual data with data in databases, match the appearance of an authorized individual with a photo in his/her document and image in the database, observe the behavior of the individual during the dialogue, monitor voice features, acquire ngerprints (palmprints), and analyze his/her documents. These functions are distributed between security personnel to minimize the time of service. The integration of additional biometric devices does not improve the authorization cycle and increase performance of the system. New design paradigms and concepts are required for the next generation of PASSes to provide reliable authorization in a short time. To achieve this, security personnel must be ef ciently supported in order to make reliable decisions on authorization in a limited time. These personnel must be effectively trained and prepared to make correct decisions in various authorization scenarios, including extreme situations. The new generation of PASSes should not only provide for the reliable identi cation of an individual, but also supply data for situational awareness and risk management support [2, 3, 5]. In particular, camou age (plastic surgery technologies) is a particular focus of interest. It is impossible to detect these disguises for altering facial features in the visible band without prior knowledge. The infrared spectrum provides useful information for detection of disguised features [6 8]. Two directions in designing PASSes can be identi ed today [1 3]. The rst direction includes approaches based on the expansion of data sources; and as a result, the burden of professional skills required of the of cer increases. In these approaches, the problems of supporting the of cer are critically simpli ed, allowing a cost-ef cient solution, but one that is not very suitable in practice. The second direction aims at high-level automated system design, where the human factor is critically reduced. This type of system is often considered as a cognitive system [9]. Our study contributes to the fundamentals of PASS design at the system level. The proposed concept of decision-making support in biometric-based PASSes utilizes techniques from biometrics, system design, decision-making, image
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