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Font-Aragones X.,TecnoCampus Mataro Maresme Edifici Universitari | Faundez-Zanuy M.,TecnoCampus Mataro Maresme Edifici Universitari | Mekyska J.,Brno University of Technology
IEEE Aerospace and Electronic Systems Magazine | Year: 2013

We have seen how to avoid the cold finger areas in order to get a better segmented TH image. These approaches are only necessary when temperatures from the finger are close to the surface. Once the TH image is well segmented we have observed different performance. © 1986-2012 IEEE. Source


Sesa-Nogueras E.,TecnoCampus Mataro Maresme Edifici Universitari | Faundez-Zanuy M.,TecnoCampus Mataro Maresme Edifici Universitari
Engineering Applications of Artificial Intelligence | Year: 2013

This paper presents a new method to generate synthetic executions of on-line words from real samples. The proposed generation method takes advantage of the characteristics of a writer recognition system developed by the authors and can be seamlessly integrated into it. Both the generation method and the recognition system consider strokes as the structural units of handwriting with words being regarded as two sequences, one of pen-up and one of pen-down strokes. Given two samples from the same word and writer, a new sample is produced by aligning their sequences of strokes and then averaging the matching pairs. Thanks to a self-organising map used to categorise strokes, the alignment and comparison of sequences of strokes are performed in a straightforward and computationally efficient way. The synthetically generated words not only retain much of the discriminative power (i.e. the capability to discriminate among writers) of the words from which they are generated, but in some cases exhibit an increased recognition performance. Also, the newly generated words allow enlarging the number of available samples in the enrolment sets that are used to build writers' models. In most cases, this enlargement has the effect to improve the performance of the recognition system. Experimenting with 320 writers and enrolment sets containing 3 real samples and 6 synthetically generated ones, verification is improved for 15 of the 16 words in the BiosecurID database, with the minimum of the detection cost function being reduced by up to a 26.5%. © 2012 Elsevier Ltd. All rights reserved. Source


Sesa-Nogueras E.,TecnoCampus Mataro Maresme Edifici Universitari | Faundez-Zanuy M.,TecnoCampus Mataro Maresme Edifici Universitari | Roure-Alcobe J.,TecnoCampus Mataro Maresme Edifici Universitari
Cognitive Computation | Year: 2015

This paper presents a gender-classification schema based on online handwriting. Using samples acquired with a digital tablet that captures the dynamics of the writing, it classifies the writer as a male or a female. The method proposed is allographic, regarding strokes as the structural units of handwriting. Strokes performed while the writing device is not exerting any pressure on the writing surface, pen-up (in-air) strokes, are also taken into account. The method is also text-dependent meaning that training and testing is done with exactly the same text. Text-dependency allows classification be performed with very small amounts of text. Experimentation, performed with samples from the BiosecurID database, yields results that fall in the range of the classification averages expected from human judges. With only four repetitions of a single uppercase word, the average rate of well-classified writers is 68 %; with sixteen words, the rate rises to an average of 72.6 %. Statistical analysis reveals that the aforementioned rates are highly significant. In order to explore the classification potential of the pen-up strokes, these are also considered. Although in this case, results are not conclusive, and an outstanding average of 74 % of well-classified writers is obtained when information from pen-up strokes is combined with information from pen-down ones. © 2015 Springer Science+Business Media New York Source

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