Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/74425
Title: Approaching the Intra-Class Variability in Multi-Script Static Signature Evaluation
Authors: Diaz, Moises 
Ferrer, Miguel A. 
Sabourin, Robert
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Verification
Recognition
Mechanisms
Online
Art
Issue Date: 2016
Journal: Proceedings - International Conference on Pattern Recognition 
Conference: 23rd International Conference on Pattern Recognition (ICPR) 
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-theart 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/10553/74425
ISSN: 1051-4651
Source: 2016 23Rd International Conference On Pattern Recognition (Icpr)[ISSN 1051-4651], p. 1147-1152, (2016)
Appears in Collections:Actas de congresos
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