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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
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.
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
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