SVC-onGoing: Signature verification competition

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 experi...

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Bibliographic Details
Authors: Tolosana Moranchel, Rubén, Gonzalez-Garcia, Carlos, 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, Vera Rodríguez, Rubén, Fiérrez Aguilar, Julián, Morales Moreno, Aythami, Ortega García, Javier
Format: article
Publication Date:2022
Country:España
Institution:Universidad Autónoma de Madrid
Repository:Biblos-e Archivo. Repositorio Institucional de la UAM
Language:English
OAI Identifier:oai:repositorio.uam.es:10486/702400
Online Access:http://hdl.handle.net/10486/702400
https://dx.doi.org/10.1016/j.patcog.2022.108609
Access Level:Open access
Keyword:Biometrics
DeepSignDB
Handwriting
Signature verification
SVC 2021
SVC-onGoing
SVC2021_EvalDB
Telecomunicaciones
Description
Summary: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

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