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 |
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