SVC-onGoing: Signature verification competition
Author
Tolosana Moranchel, Rubén




Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
ElsevierDate
2022-02-24Citation
10.1016/j.patcog.2022.108609
Pattern Recognition 127 (2022): 108609
ISSN
0031-3203 (print)DOI
10.1016/j.patcog.2022.108609Funded by
This work has been supported by projects: PRIMA (H2020- MSCA-ITN-2019-860315), TRESPASS-ETN (H2020-MSCA-ITN-2019- 860813), INTER-ACTION (PID2021-126521OB-I00 MICINN/FEDER), Orange Labs, and by UAM-CecabankProject
info:eu-repo/grantAgreement/EC/H2020/860315/EU/PriMa-ITN; info:eu-repo/grantAgreement/EC/H2020/860813/EU/TReSPAsS-ETN; Gobierno de España. PID2021-126521OB-I00Editor's Version
https://doi.org/10.1016/j.patcog.2022.108609Subjects
Biometrics; DeepSignDB; Handwriting; Signature verification; SVC 2021; SVC-onGoing; SVC2021_EvalDB; TelecomunicacionesRights
14 pag.
Esta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
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
Files in this item
Google Scholar:Tolosana Moranchel, Rubén
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Vera Rodríguez, Rubén
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Gonzalez-Garcia, Carlos
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Fiérrez Aguilar, Julián
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Morales Moreno, Aythami
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Ortega García, Javier
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Carlos Ruiz-Garcia, Juan
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Romero-Tapiador, Sergio
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Rengifo, Santiago
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Caruana, Miguel
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Jiang, Jiajia
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Lai, Songxuan
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Jin, Lianwen
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Zhu, Yecheng
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Galbally, Javier
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Diaz, Moises
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Angel Ferrer, Miguel
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Gomez-Barrero, Marta
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Hodashinsky, Ilya
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Sarin, Konstantin
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Slezkin, Artem
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Bardamova, Marina
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Svetlakov, Mikhail
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Saleem, Mohammad
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Lia Szcs, Cintia
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Kovari, Bence
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Pulsmeyer, Falk
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Wehbi, Mohamad
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Zanca, Dario
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Ahmad, Sumaiya
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Mishra, Sarthak
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Jabin, Suraiya
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