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首页> 外文会议>International Conference on Advances in Big Data, Computing and Data Communication Systems >Online Signature Verification through Scaled Histogram of Oriented Gradients
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Online Signature Verification through Scaled Histogram of Oriented Gradients

机译：通过比例梯度直方图进行在线签名验证

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摘要

Since signatures are an integral part of our everyday lives, that represent a legally binding seal of approval; it is essential for them to be verified accurately. Online signature verification is used to confirm an individual’s identity based on realtime characteristics that are compiled during the act of signing. It is often used to reduce fraud in institutions such as banks and other credit service providers, since it offers a robust alternative to the traditional offline signature verification. This study aims to complement existing efforts in this field by employing Histogram of Oriented Gradients (HOGs) as a feature extraction mechanism that feeds into a template matching exercise based on Normalized Cross Correlation (NCC). The scale at which HOGs are applied is controlled in order to investigate the trade-off between granularity and verification effectiveness. Accuracies of 78% and 70% are obtained on the SVC2004 dataset for complete and half signature coverage respectively. This highlights the importance of global context when using realtime features, since it appears that useful information is lost when using separate HOGs for the first and second half of an online signature.
机译：由于签名是我们日常生活中不可或缺的一部分，因此代表了具有法律约束力的批准印章;对他们进行准确验证至关重要。在线签名验证用于基于在签名过程中收集的实时特征来确认个人的身份。它通常用于减少银行和其他信贷服务提供商等机构的欺诈行为，因为它提供了传统脱机签名验证的可靠替代方案。这项研究旨在通过使用定向梯度直方图（HOG）作为特征提取机制来补充该领域中的现有工作，该机制将信息输入到基于归一化互相关（NCC）的模板匹配练习中。控制HOG的应用规模，以研究粒度与验证有效性之间的权衡。在SVC2004数据集上，分别达到完整和一半签名覆盖率的准确度分别为78％和70％。这突出了使用实时功能时全局上下文的重要性，因为当在在线签名的前半部分和后半部分使用单独的HOG时，似乎会丢失有用的信息。

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