Search

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).

This paper reviews the latest results in the field of online signature verification and summarizes the previously published major surveys and also over 30 papers from the last decade. We examine the steps of the verification process and show the most popular approaches used. Our results show that alignment and scaling are the most common methods used in preprocessing. Position, velocity, and pressure are the most commonly used measures for feature extraction while dynamic time warping is the most commonly used approach for verification. A comparison between these methods using different databases concludes this work. The error rate varies between 0.77% to 7.13%, with an average of 2.94%. The results and comparisons published in this paper may help researchers choose the most promising approaches for their systems.