This article presents SVC-onGoing, 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 DeepSignDB and SVC2021_EvalDB, 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.
翻译:本篇文章展示了SVC在线签名核查,这是目前对SVC在线签名核查的一种竞争,研究人员可以在使用大型公共数据库(如DeepSignDB和SVC2021_EvalDB)和标准实验协议等大型公共数据库的公开共同平台上,轻松地根据最新状态对其系统进行基准测试。SVC在线在线签名核查的基础是ICDAR 2021在线签名核查竞赛(SVC 2021),该竞赛已经扩展,允许参与者随时参加。SVC在线签名核查的目的是通过大型公共数据库,在公开平台上,根据最新状态(办公室/移动)和书面投入(Stylus/Fingger),评估在线签名系统对最新状态的系统的限制。在竞争中考虑三项不同的任务,即模拟现实情景,即随机和熟练的伪造,每项任务同时考虑SVC在线签名核查(SVC on Going)取得的结果证明,与传统方法相比,深层学习方法有很大的潜力。特别是,最佳签名核查系统获得了3.33%(Task 1)、7.41(Tylus)和书型签名系统6-GO2期间的SO-roup-listrationS-listra-lishew的绩效研究和S-view2)。