ICDAR 2021 Competition on On-Line Signature Verification

Tolosana R, Vera-Rodriguez R, Gonzalez-Garcia C, Fierrez J, Rengifo S, Morales A, Ortega-Garcia J, Carlos Ruiz-Garcia J, Romero-Tapiador S, Jiang J, Lai S, Jin L, Zhu Y, Galbally J, Diaz M, Ferrer MA, Gomez-Barrero M, Hodashinsky I, Sarin K, Slezkin A, Bardamova M, Svetlakov M, Saleem M, Szücs CL, Kovari B, Pulsmeyer F, Wehbi M, Zanca D, Ahmad S, Mishra S, Jabin S (2021)

Publication Type: Conference contribution

Publication year: 2021

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 12824 LNCS

Pages Range: 723-737

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Lausanne, CHE

ISBN: 9783030863364

DOI: 10.1007/978-3-030-86337-1_48

Abstract

This paper describes the experimental framework and results of the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021). The goal of SVC 2021 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 2021 prove the high potential of deep learning methods. In particular, the best on-line signature verification system of SVC 2021 obtained Equal Error Rate (EER) values of 3.33% (Task 1), 7.41% (Task 2), and 6.04% (Task 3). SVC 2021 will be established as an on-going competition (https://sites.google.com/view/SVC2021 ), 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 DeepSignDB (https://github.com/BiDAlab/DeepSignDB ) and SVC2021_EvalDB (https://github.com/BiDAlab/SVC2021_EvalDB ), and standard experimental protocols.

Authors with CRIS profile

Falk Pulsmeyer Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Mohamad Wehbi Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Dario Zanca Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics)

Involved external institutions

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

How to cite

APA:

Tolosana, R., Vera-Rodriguez, R., Gonzalez-Garcia, C., Fierrez, J., Rengifo, S., Morales, A.,... Jabin, S. (2021). ICDAR 2021 Competition on On-Line Signature Verification. In Josep Lladós, Daniel Lopresti, Seiichi Uchida (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 723-737). Lausanne, CHE: Springer Science and Business Media Deutschland GmbH.

MLA:

Tolosana, Ruben, et al. "ICDAR 2021 Competition on On-Line Signature Verification." Proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, Lausanne, CHE Ed. Josep Lladós, Daniel Lopresti, Seiichi Uchida, Springer Science and Business Media Deutschland GmbH, 2021. 723-737.

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