Please use this identifier to cite or link to this item:
http://hdl.handle.net/10553/37175
Title: | Approaching the intra-class variability in multi-script static signature evaluation | Authors: | Diaz, Moises Ferrer, Miguel A. Sabourin, Robert |
UNESCO Clasification: | 120304 Inteligencia artificial 3307 Tecnología electrónica |
Keywords: | Verification Recognition Mechanisms Online Art |
Issue Date: | 2016 | Publisher: | Institute of Electrical and Electronics Engineers (IEEE) | Journal: | Proceedings - International Conference on Pattern Recognition | Conference: | 23rd International Conference on Pattern Recognition, ICPR 2016 | Abstract: | As an emerging issue, multi-script signature verification is a recent challenge for current Automatic Signature Verification (ASV) systems. Relevant differences are presented in the morphology and lexicon of the signature images written in different scripts, such as used symbols, shape of the signatures, legibility, etc. These peculiarities could reduce the success of ASV systems, especially those which were originally designed for only one kind of script. However, one common feature among scripts in ASV is the fact that the greater the number of signatures that are used for training, the better the expected performance. In this work, we propose a method inspired by observations from the neuromotor equivalence theory to artificially enlarge the signature images used to train a state-of-the-art static signature classifier. Experimental results are obtained by using three static signature datasets derived from completely different scripts: Western, Bengali and Devanagari. Our results suggest that the cognitive-inspired model, which aims to duplicate static signatures, tends toward intra-class variability of signatures written in different scripts; the model's beneficial impact is seen in signature verification tests. | URI: | http://hdl.handle.net/10553/37175 | ISBN: | 978-1-5090-4847-2 | ISSN: | 1051-4651 | DOI: | 10.1109/ICPR.2016.7899791 | Source: | Proceedings - International Conference on Pattern Recognition [ISSN 1051-4651], v. 0 (7899791), p. 1147-1152 |
Appears in Collections: | Actas de congresos |
SCOPUSTM
Citations
20
checked on Oct 6, 2024
WEB OF SCIENCETM
Citations
9
checked on Apr 25, 2021
Page view(s)
81
checked on Sep 7, 2024
Google ScholarTM
Check
Altmetric
Share
Export metadata
Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.