SVC-onGoing: Signature verification competition

This article presents SVC-onGoing1, an on-going competition for on-line signature verification where re-searchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases, such as DeepSignDB(2) and SVC2021_EvalDB(3), and standard experimen-tal protocols. SVC-onGoing is based on the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021), which has been extended to allow participants anytime. The goal of SVC-onGoing is to eval-uate the limits of on-line signature verification systems on popular scenarios (office/mobile) and writing inputs (stylus/finger) through large-scale public databases. Three different tasks are considered in the competition, simulating realistic scenarios as both random and skilled forgeries are simultaneously con-sidered on each task. The results obtained in SVC-onGoing prove the high potential of deep learning methods in comparison with traditional methods. In particular, the best signature verification system has obtained Equal Error Rate (EER) values of 3.33% (Task 1), 7.41% (Task 2), and 6.04% (Task 3). Future stud-ies in the field should be oriented to improve the performance of signature verification systems on the challenging mobile scenarios of SVC-onGoing in which several mobile devices and the finger are used during the signature acquisition. (C) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

Automated summary

This article introduces SVC-onGoing1, an ongoing competition for online signature verification, where researchers can benchmark their systems against the state of the art using large-scale public databases and standard experimental protocols. The results of SVC-onGoing demonstrate the high potential of deep learning methods compared to traditional methods in signature verification.