ICDAR 2021 Competition on On-Line Signature Verification

被引：11

作者：

Tolosana, Ruben ^{[1
]}

Vera-Rodriguez, Ruben ^{[1
]}

Gonzalez-Garcia, Carlos ^{[1
]}

Fierrez, Julian ^{[1
]}

Rengifo, Santiago ^{[1
]}

Morales, Aythami ^{[1
]}

Ortega-Garcia, Javier ^{[1
]}

Carlos Ruiz-Garcia, Juan ^{[1
]}

Romero-Tapiador, Sergio ^{[1
]}

Jiang, Jiajia ^{[2
]}

Lai, Songxuan ^{[2
]}

Jin, Lianwen ^{[2
,3
]}

Zhu, Yecheng ^{[2
]}

Galbally, Javier ^{[4
]}

Diaz, Moises ^{[5
]}

Angel Ferrer, Miguel ^{[6
]}

Gomez-Barrero, Marta ^{[7
]}

Hodashinsky, Ilya ^{[8
]}

Sarin, Konstantin ^{[8
]}

Slezkin, Artem ^{[8
]}

Bardamova, Marina ^{[8
]}

Svetlakov, Mikhail ^{[8
]}

Saleem, Mohammad ^{[9
]}

Szucs, Cintia Lia ^{[9
]}

Kovari, Bence ^{[9
]}

Pulsmeyer, Falk ^{[10
]}

Wehbi, Mohamad ^{[10
]}

Zanca, Dario ^{[10
]}

Ahmad, Sumaiya ^{[11
]}

Mishra, Sarthak ^{[11
]}

Jabin, Suraiya ^{[11
]}

机构：

[1] UAM, Biometr & Data Pattern Analyt Lab, Madrid, Spain

[2] South China Univ Technol, Guangzhou, Peoples R China

[3] Guangdong Artificial Intelligence & Digital Econ, Guangzhou, Peoples R China

[4] European Commiss Joint Res Ctr, Ispra, Italy

[5] Univ Atlantico Medio, Las Palmas Gran Canaria, Spain

[6] Univ Las Palmas Gran Canaria, Las Palmas Gran Canaria, Spain

[7] Hsch Ansbach, Ansbach, Germany

[8] Tomsk State Univ Control Syst & Radioelect, Tomsk, Russia

[9] Budapest Univ Technol & Econ, Budapest, Hungary

[10] FAU, Machine Learning & Data Analyt Lab, Erlangen, Germany

[11] Jamia Millia Islamia, New Delhi, India

来源：

DOCUMENT ANALYSIS AND RECOGNITION, ICDAR 2021, PT IV | 2021年 / 12824卷

基金：

欧盟地平线“2020”;

关键词：

SVC; 2021; Biometrics; Handwriting; On-line signature; Benchmark; DeepSignDB; SVC2021_EvalDB; Deep learning; SENSOR INTEROPERABILITY; FUSION;

D O I：

10.1007/978-3-030-86337-1_48

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 competition (https://sites.google.com/view/SVC2021), 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 (https://github.com/BiDAlab/DeepSignDB) and SVC2021 EvalDB (https://github.com/BiDAlab/SVC2021_EvalDB), and standard experimental protocols.

引用

页码：723 / 737

页数：15

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