Handwriting: Feature Correlation Analysis for Biometric Hashes
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Handwriting: Feature Correlation Analysis for Biometric Hashes Claus Vielhauer Multimedia Communications Lab (KOM), Darmstadt University of Technology, 64283 Darmstadt, Germany Platanista GmbH, 06846 Dessau, Germany Faculty of Computer Science, Otto-von-Guericke University, 39106 Magdeburg, Germany Email: [email protected]
Ralf Steinmetz Multimedia Communications Lab (KOM), Darmstadt University of Technology, 64283 Darmstadt, Germany Email: [email protected] Received 17 November 2002; Revised 9 September 2003 In the application domain of electronic commerce, biometric authentication can provide one possible solution for the key management problem. Besides server-based approaches, methods of deriving digital keys directly from biometric measures appear to be advantageous. In this paper, we analyze one of our recently published specific algorithms of this category based on behavioral biometrics of handwriting, the biometric hash. Our interest is to investigate to which degree each of the underlying feature parameters contributes to the overall intrapersonal stability and interpersonal value space. We will briefly discuss related work in feature evaluation and introduce a new methodology based on three components: the intrapersonal scatter (deviation), the interpersonal entropy, and the correlation between both measures. Evaluation of the technique is presented based on two data sets of different size. The method presented will allow determination of effects of parameterization of the biometric system, estimation of value space boundaries, and comparison with other feature selection approaches. Keywords and phrases: biometrics, signature verification, feature evaluation, feature correlation, cryptographic key management, handwriting, information entropy.
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MOTIVATION
Today, a wide spectrum of technologies for user identification and verification exists and a great number of the systems that have been published are based on long-term research. The basic concept behind all biometric systems is the idea to make use of machine-measurable traits to distinguish persons. In order to be adequate for this process, a number of requirements must be fulfilled by a human trait feature, see [1]. For our working context, the following four are of main interest: (i) uniqueness: the feature must vary to a reasonable extent amongst a wide set of individuals (intervariability); (ii) constancy (permanence): the feature must vary as little as possible for each individual (intravariability); (iii) distribution (universality): the feature must be available for as many potential users as possible; (iv) measurability (collectability): the feature must be electronically measurable.
Biometric characteristics, which fulfill the above requirements, can be classified in a number of ways, for example, see [2, 3]. One common approach is to divide into measures, which are either originating from a physiological or a behavioral trait of subjects, although it has been shown that every process of capturing biomet
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