Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/107491
Título: ICDAR 2021 Competition on On-Line Signature Verification
Autores/as: Tolosana, Ruben
Vera-Rodriguez, Ruben
Gonzalez-Garcia, Carlos
Fierrez, Julian
Rengifo, Santiago
Morales Moreno,Aythami 
Ortega-Garcia, Javier
Ruiz-Garcia, JuanCarlos
Romero Tapiador, Sergio
Jiajia Jiang
Songxuan Lai
Lianwen Jin
Yecheng Zhu
Galbally, Javier
Diaz, Moises 
Ferrer Ballester, Miguel Ángel 
Gomez Barrero, Marta
Hodashinsky, Ilya
Sarin, Konstantin
Slezkin, Artem
Bardamova, Marina
Svetlakov, Mikhail
Saleem, Mohammad
Szucs, Cintia Lia
Kovari, Bence
Pulsmeyer, Falk
Wehbi, Mohamad
Zanca, Dario
Ahmad, Sumaiya
Mishra, Sarthak
Jabin, Suraiya
Clasificación UNESCO: 570102 Documentación automatizada
2405 Biometría
Palabras clave: SVC 2021
On-Line Signature
Biometrics
Handwriting
Benchmark, et al.
Fecha de publicación: 2021
Proyectos: PriMa (860315 )
TReSPAsS-ETN (860813)
BIBECA (RTI2018-101248-B-I00 MINECO/FEDER)
Conferencia: 16th International Conference on Document Analysis and Recognition (ICDAR 2021) 
Resumen: 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 competition12, 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 DeepSignDB13 and SVC2021 EvalDB14, and standard experimental protocols.
URI: http://hdl.handle.net/10553/107491
Fuente: Proceedings International Conference on Document Analysis and Recognition 2021
Colección:Actas de congresos
miniatura
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