Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0031-3203(02)00061-4
Title: Off-line signature verification by the tracking of feature and stroke positions
Authors: Fang, B. 
Leung, C.H.
Tang, Y.Y.
Tse, K.W.
Kwok, P.C.K.
Wong, Y.K.
Keywords: Elastic matching
Feature tracking
Handwriting recognition
Off-line system
Signature verification
Issue Date: Jan-2003
Citation: Fang, B., Leung, C.H., Tang, Y.Y., Tse, K.W., Kwok, P.C.K., Wong, Y.K. (2003-01). Off-line signature verification by the tracking of feature and stroke positions. Pattern Recognition 36 (1) : 91-101. ScholarBank@NUS Repository. https://doi.org/10.1016/S0031-3203(02)00061-4
Abstract: There are inevitable variations in the signature patterns written by the same person. The variations can occur in the shape or in the relative positions of the characteristic features. In this paper, two methods are proposed to track the variations. Given the set of training signature samples, the first method measures the positional variations of the one-dimensional projection profiles of the signature patterns; and the second method determines the variations in relative stroke positions in the two-dimension signature patterns. The statistics on these variations are determined from the training set. Given a signature to be verified, the positional displacements are determined and the authenticity is decided based on the statistics of the training samples. For the purpose of comparison, two existing methods proposed by other researchers were implemented and tested on the same database. Furthermore, two volunteers were recruited to perform the same verification task. Results show that the proposed system compares favorably with other methods and outperforms the volunteers. © 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Source Title: Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/114620
ISSN: 00313203
DOI: 10.1016/S0031-3203(02)00061-4
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