A Comparison of Classi cation- and Indexing-Based Approaches in .NET

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A Comparison of Classi cation- and Indexing-Based Approaches
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8. B. Bhanu and X. Tan, Fingerprint indexing based on novel features of minutiae triplets, IEEE Trans. Pattern Anal. Mach. Intell. 25(5):616 622, 2003. 9. X. Tan, B. Bhanu and, Y. Lin, Learning composite operators for ngerprint classi cation, Proceeding of the International Conference on Audio- and Video-Based Biometric Person Authentication, June 2003, pp. 318 326. 10. D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition, Springer, Berlin, 2003. 11. B. Bhanu and X. Tan, Learned templates for feature extraction in ngerprint images, Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2:591 596, 2001. 12. A. M. Bazen and S. H. Gerez, Systematic methods for the computation of the directional elds and singular points of ngerprints, IEEE Trans. Pattern Anal. Mach. Intell. 24(7):905 919, 2002. 13. X. Tan, B. Bhanu, and Y. Lin, Fingerprint classi cation based on learned features, IEEE Trans. Systems, Man Cybern. Part C (Special Issue on Biometrics) 35(3):287 300, 2005. 14. B. Bhanu, Y. Lin, and K. Krawiec, Evolutionary Synthesis of Pattern Recognition Systems, Monograph in Computer Science, Springer, Berlin, 2006. 15. B. Bhanu, M. Boshra, and X. Tan, Logical templates for feature extraction in ngerprint images, Proc. Int. Conf. Pattern Recognit. 3:850 854, 2000. 16. S. Prabhakar, A. K. Jain, and S. Pankanti, Learning ngerprint minutiae location and type, Pattern Recognit. 36(8):1847 1857, 2003.
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Electrocardiogram (ECG) Biometric for Robust Identi cation and Secure Communication
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Francis Minhthang Bui, Foteini Agra oti, and Dimitrios Hatzinakos
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INTRODUCTION
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As a medical diagnostic technique proposed by Willem Einthoven in the early 1900s, the electrocardiogram (ECG) has a relatively long and illustrious history. It has since been acknowledged as an indispensable tool in the detection and treatment of various cardiac disorders [1, 2]. More recently, the ECG has ful lled a rather unlikely niche, as a purveyor of security and privacy in the form of a biometric [3, 4]. In this chapter, we examine the various implications and technical challenges of using the ECG as a biometric. Speci cally, we survey and propose novel signal processing techniques that seek to not only establish the status of the ECG as an indisputable xture in biometric research, but also reinforce its versatile utility, such as in alleviating the resource consumption in certain communication networks.
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Security and Privacy Motivations
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Security and privacy are intertwined issues that have long been of prime importance in society. It has even been suggested that the combination of security and privacy is
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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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Electrocardiogram (ECG) Biometric for Robust Identi cation
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tantamount to one of the cornerstones of democracy itself [5]. It is then perhaps no coincidence that we can trace the precursors of modern cryptography and steganography to as far back as possibly even earlier than Ancient Greece itself [6, 7], the birthplace of democracy. Methods for providing security have evolved tremendously and, to date, encompass a wide range of solutions, based mainly on number-theoretic problems [6, 8]. Despite the existence of various strong cryptographic algorithms, some with provable security or even perfect secrecy [8], the search for an applicable security solution seems to be perpetual. This is because, while a method may intrinsically demonstrate information-theoretic security, it may not be practical or suitable for a speci c application. For instance, the computational requirements and the system assumptions may not be achievable in a particular scenario, due to resource scarcity or other practical issues.
Security and Privacy Solutions with Biometrics
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In this context, the advent of biometric methods represents an encouraging watershed event, with the potential to deliver practical solutions to security and privacy. While certainly not a panacea, biometrics address many important issues, including cost effectiveness, user convenience, and good security. On the other hand, biometrics differ rather signi cantly from conventional signals, being more noisy and variant. Despite these technical challenges, it is almost a unanimous consensus that biometrics will remain an important stakeholder in security solutions for the foreseeable future. This is because biometric systems have unique and advantageous properties, offering novelties not available with conventional password-based systems [5, 9]. In this chapter, biometric systems based on the electrocardiogram signals will be examined. The ECG biometric presents some speci c dif culties, including baseline wandering and a requirement of accurate timing synchronization. At the same time, the ECG and other related cardiovascular signals have been found to be a unique class of biometrics that aptly ful lls the speci c requirements of the so-called body sensor networks (BSN) [10 12].
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