Publication: Data augmentation for signature images in online verification systems
Дата
2020
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© Springer Nature Switzerland AG 2020.One of the main problems of designing a handwritten signature online verification system is a small number of signatures committed by the user for training. To solve this problem, ways of expanding dataset size based on existing authenticated signatures might be proposed. The research proposes a new technique for generating dynamic signatures based on the original sample. The resulting sample simulates real signature forms and letter-style characteristics. Artificially created genuine and fake samples based on the author’s and intruder’s signatures are used to train the classifier, which can improve the accuracy of training on the original sample of a small size. Handwritten signature data augmentation methods were investigated with the aim of further development in more efficient handwritten verification algorithm.
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Beresneva, A. Data augmentation for signature images in online verification systems / Beresneva, A., Epishkina, A. // Lecture Notes in Computational Vision and Biomechanics. - 2020. - 32. - P. 105-112. - 10.1007/978-3-030-21726-6_10
URI
https://www.doi.org/10.1007/978-3-030-21726-6_10
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https://openrepository.mephi.ru/handle/123456789/20024
https://www.scopus.com/record/display.uri?eid=2-s2.0-85070555670&origin=resultslist
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000507991800010
https://openrepository.mephi.ru/handle/123456789/20024