Revealing Reliable Signatures by Learning Top-Rank Pairs
Revealing Reliable Signatures by Learning Top-Rank Pairs
复制标题
通过学习顶级对来揭示可靠的签名
DOI：
10.1007/978-3-031-06555-2_22
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Uchida Seiichi
中科院分区：
文献类型：
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作者：
Ji Xiaotong;Zheng Yan;Suehiro Daiki;Uchida Seiichi
签名验证作为一项重要的实用文档分析任务，一直是机器学习和模式识别领域的研究热点。在确认金融文件和法律的文书等特定场景中，确保签名的绝对可靠性是重中之重。在这项工作中，我们提出了一种新的方法来学习“顶级对”的独立于作者的离线签名验证任务。通过该方案，可以最大限度地增加绝对可靠签名的数量。更确切地说，我们学习顶级对的方法旨在将每个样本与真正的参考签名配对后，将阳性样本推到阴性样本之外。在实验中，使用BHSig-B和BHSig-H数据集进行评估，在该数据集上，所提出的模型实现了压倒性的更好的pos@top（绝对顶部阳性样本与所有阳性样本的比率），同时在曲线下面积（AUC）和准确性方面表现出令人鼓舞的性能。
Signature verification, as a crucial practical documentation analysis task, has been continuously studied by researchers in machine learning and pattern recognition fields. In specific scenarios like confirming financial documents and legal instruments, ensuring the absolute reliability of signatures is of top priority. In this work, we proposed a new method to learn “top-rank pairs” for writer-independent offline signature verification tasks. By this scheme, it is possible to maximize the number of absolutely reliable signatures. More precisely, our method to learn top-rank pairs aims at pushing positive samples beyond negative samples, after pairing each of them with a genuine reference signature. In the experiment, BHSig-B and BHSig-H datasets are used for evaluation, on which the proposed model achieves overwhelming better pos@top (the ratio of absolute top positive samples to all of the positive samples) while showing encouraging performance on both Area Under the Curve (AUC) and accuracy.