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Several studies showed that EEG signal of Alzheimer's disease patients is less complex than that of healthy subjects. In this article, we propose to characterize the complexity of the EEG signal by an entropy measure based on local density estimation by a Hidden Markov Model. We first show that this measure leads to consistent results qualitatively and quantitatively (in terms of classification accuracy)...
In this paper, we present the main results of the BioSecure Signature Evaluation Campaign (BSEC'2009). The objective of BSEC'2009 was to evaluate different online signature algorithms on two tasks: the first one aims at studying the influence of acquisition conditions (digitizing tablet or PDA) on systems' performance; the second one aims at studying the impact of information content in signatures...
In this paper, we present the main results of the BioSecure Signature Evaluation Campaign (ESRA'2011). The objective of ESRA'2011 is to evaluate through two different tasks the resistance of different online signature systems to skilled forgeries categorized automatically according to their quality. Task 1 aims at studying with only coordinate time functions the influence of acquisition conditions...
This paper proposes two systems for offline signature verification based on a global and on a local approach respectively. The features used consist of different kinds of geometrical, statistical and structural features. For comparison purposes, we used two baseline systems (global and local), both based on a larger number of features encoding the orientations of the strokes using mathematical morphology...
In this work, we study different combinations of the five time functions captured by a digitizer in presence or not of time variability. To this end, we propose two criteria independent of the classification step: personal entropy, introduced in our previous works and an intra-class variability measure based on dynamic time warping. We confront both criteria to system performance using a hidden Markov...
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...
In this article, we propose an original way to characterize information content in online signatures through a client-entropy measure based on local density estimation by a hidden Markov model. We show that this measure can be used to categorize signatures in visually coherent classes that can be related to complexity and variability criteria. Besides, the generated categories are coherent across...
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