Automatic processing of offline signature verification (in general) can be considered as a low-cost solution to problems in biometrics in comparison with other solutions (e. g. fingerprint, face verification, etc.). This study aims to present a novel writer-dependent approach to verifying an individual’s signature through offline image patches of their handwriting. The proposed approach is based on hierarchical one-class convolutional neural network for learning only genuine signatures with different feature levels. Since forgeries are not available for each user enrolled in a real application scenario, this study considers signature verification as a one-class problem. In addition, to achieve a clear structure in image, designing hierarchical network architecture based on the coarse-to-fine principle can lead to more precise results. With lower-level features, the network presents a higher visual quality at the boundary area revealing similarities between genuine signatures, while higher-level features can discriminate the quality of the pen strokes to predict forgeries from genuine signatures. The presented system was tested on two Persian databases (PHBC and UTSig) as well as two Latin databases (MCYT-75 and CEDAR). The results of the analyses produced by this method were generally better and more exact in terms of the four signature databases compared with the present state-of-the-art results.

中文翻译：

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使用一类分层深度学习的基于补丁的离线签名验证

与其他解决方案（例如指纹，面部验证等）相比，脱机签名验证的自动处理（通常）可被视为针对生物识别问题的低成本解决方案。这项研究旨在提出一种新颖的依赖作者的方法，以通过其笔迹的离线图像补丁来验证个人的签名。所提出的方法基于分层的一类卷积神经网络，仅用于学习具有不同特征级别的真实签名。由于无法为在实际应用场景中注册的每个用户提供伪造品，因此本研究将签名验证视为一类问题。另外，为了获得清晰的图像结构，基于粗糙到精细的原理设计分层网络体系结构可以带来更精确的结果。通过使用较低级别的功能，网络可以在边界区域呈现更高的视觉质量，从而揭示出真实签名之间的相似性，而较高级别的功能可以区分笔触的质量，从而根据真实签名来预测伪造。该系统在两个波斯数据库（PHBC和UTSig）以及两个拉丁数据库（MCYT-75和CEDAR）上进行了测试。与当前的最新技术结果相比，就四个签名数据库而言，通过这种方法产生的分析结果通常更好，更准确。该系统在两个波斯数据库（PHBC和UTSig）以及两个拉丁数据库（MCYT-75和CEDAR）上进行了测试。与目前的最新结果相比，就四个签名数据库而言，用这种方法产生的分析结果通常更好，更准确。该系统在两个波斯数据库（PHBC和UTSig）以及两个拉丁数据库（MCYT-75和CEDAR）上进行了测试。与当前的最新技术结果相比，就四个签名数据库而言，通过这种方法产生的分析结果通常更好，更准确。