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A Novel Personal Entropy Measure confronted with Online Signature Verification Systems’ Performance

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Source: IEEE 2nd International Conference on Biometrics: Theory, Applications and Systems (BTAS’2008), Proceedings on CD Rom, Washington, USA, Septembre 2008
Authors: N. Houmani, S. Garcia-Salicetti, B. Dorizzi
Keywords:

Abstract: Abstract—In this paper, we study the relationship between a
novel personal entropy measure for online signatures and the
performance of several state-of-the-art classifiers. The entropy
measure is based on local density estimation by a Hidden
Markov Model. We show that there is a clear relationship
between such entropy measure of a person’s signature and the
behavior of the classifier. We carry out this study on a Dynamic
Time Warping classifier, a Gaussian Mixture Model and a
Hidden Markov Model as well. It is worth noticing that the
HMM classifier differs from the HMM used for entropy
computation. Signatures were split into three categories
according to their entropy value. These categories are coherent
across four different databases of around 100 persons each:
BIOMET, MCYT-100, BioSecure data subsets DS2 and DS3.
We studied the impact of such categories on classifier’s
performance with a larger signature data subset of DS3, of 430
persons.

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