SVC-onGoing: Signature verification competition

Tolosana R, Vera-Rodriguez R, Gonzalez-Garcia C, Fierrez J, Morales A, Ortega-Garcia J, Carlos Ruiz-Garcia J, Romero-Tapiador S, Rengifo S, Caruana M, Jiang J, Lai S, Jin L, Zhu Y, Galbally J, Diaz M, Angel Ferrer M, Gomez-Barrero M, Hodashinsky I, Sarin K, Slezkin A, Bardamova M, Svetlakov M, Saleem M, Lia Szcs C, Kovari B, Pulsmeyer F, Wehbi M, Zanca D, Ahmad S, Mishra S, Jabin S (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Pattern Recognition Elsevier

Book Volume: 127

Article Number: 108609

DOI: 10.1016/j.patcog.2022.108609

Abstract

This article presents SVC-onGoing1, an on-going competition for on-line signature verification where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases, such as DeepSignDB2 and SVC2021_EvalDB3, and standard experimental 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 evaluate 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 considered 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 studies 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.

Authors with CRIS profile

Falk Pulsmeyer Machine Learning and Data Analytics Lab Mohamad Wehbi Machine Learning and Data Analytics Lab Dario Zanca Machine Learning and Data Analytics Lab

Involved external institutions

Budapest University of Technology and Economics (BME) / Budapesti Műszaki és Gazdaságtudományi Egyetem HU Hungary (HU) Jamia Millia Islamia IN India (IN) Tomsk State University of Control Systems and Radio-Electronics (TUSUR) / То́мский госуда́рственный университе́т систе́м управле́ния и радио́электро́ники (ТУСУ́Р) RU Russian Federation (RU) South China University of Technology (SCUT) / 华南理工大学 CN China (CN) Hochschule Ansbach DE Germany (DE) Universidad de Las Palmas Gran Canaria ES Spain (ES) European Commission - Joint Research Centre (JRC) NL Netherlands (NL)

How to cite

APA:

Tolosana, R., Vera-Rodriguez, R., Gonzalez-Garcia, C., Fierrez, J., Morales, A., Ortega-Garcia, J.,... Jabin, S. (2022). SVC-onGoing: Signature verification competition. Pattern Recognition, 127. https://dx.doi.org/10.1016/j.patcog.2022.108609

MLA:

Tolosana, Ruben, et al. "SVC-onGoing: Signature verification competition." Pattern Recognition 127 (2022).

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