SVC-onGoing: Signature verification competition. (July 2022)

• Record Type: Journal Article
• Authors: Tolosana, Ruben; Vera-Rodriguez, Ruben; Gonzalez-Garcia, Carlos; Fierrez, Julian; Morales, Aythami; Ortega-Garcia, Javier; Carlos Ruiz-Garcia, Juan; Romero-Tapiador, Sergio; Rengifo, Santiago; Caruana, Miguel; Jiang, Jiajia; Lai, Songxuan; Jin, Lianwen; Zhu, Yecheng; Galbally, Javier; Diaz, Moises; Angel Ferrer, Miguel; Gomez-Barrero, Marta; Hodashinsky, Ilya; Sarin, Konstantin; Slezkin, Artem; Bardamova, Marina; Svetlakov, Mikhail; Saleem, Mohammad; Lia Szcs, Cintia; Kovari, Bence; Pulsmeyer, Falk; Wehbi, Mohamad; Zanca, Dario; Ahmad, Sumaiya; Mishra, Sarthak; Jabin, Suraiya;
• Is Part Of: Pattern recognition. Volume 127(2022)
• Publication Date: 2022-07
• Subjects: SVC-onGoing -- SVC 2021 -- Biometrics -- Handwriting -- Signature verification -- DeepSignDB -- SVC2021_EvalDB; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.108609
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: SVC-onGoing is the first on-going competition for on-line signature verification. Researchers can easily benchmark their systems using public databases and platform. Fair comparison of the state of the art: traditional vs deep learning approaches. Analysis of popular scenarios (office/mobile) and writing inputs (stylus/finger). Analysis of multiple types of attacks. Abstract: This article presents SVC-onGoing 1, 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 DeepSignDB 2 and SVC2021_EvalDB 3, 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.