In automatic signature verification, questioned specimens are usually compared with reference signatures. In writer-dependent schemes, a number of reference signatures are required to build up the individual signer model while a writer-independent system requires a set of reference signatures from several signers to develop the model of the system. This paper addresses the problem of automatic signature verification when no reference signatures are available. The scenario we explore consists of a set of signatures, which could be signed by the same author or by multiple signers. As such, we discuss three methods which estimate automatically the common authorship of a set of off-line signatures. The first method develops a score similarity matrix, worked out with the assistance of duplicated signatures; the second uses a feature-distance matrix for each pair of signatures; and the last method introduces pre-classification based on the complexity of each signature. Publicly available signatures were used in the experiments, which gave encouraging results. As a baseline for the performance obtained by our approaches, we carried out a visual Turing Test where forensic and non-forensic human volunteers, carrying out the same task, performed less well than the automatic schemes.

中文翻译：

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在不使用参考签名的情况下通过脱机自动签名验证研究签名的共同作者身份

在自动签名验证中，通常将有疑问的样本与参考签名进行比较。在依赖于编写者的方案中，需要大量参考签名来构建单独的签名者模型，而与编写者无关的系统需要来自多个签名者的一组参考签名来开发系统模型。当没有参考签名可用时，本文解决了自动签名验证的问题。我们探索的场景由一组签名组成，可以由同一作者或多个签名者签名。因此，我们讨论了三种自动估计一组脱机签名的共同作者身份的方法。第一种方法是在重复签名的帮助下得出分数相似度矩阵。第二个对每对签名使用特征距离矩阵；最后一种方法则根据每个签名的复杂度引入了预分类。实验中使用了公开可用的签名，得出了令人鼓舞的结果。作为通过我们的方法获得的性能的基准，我们进行了视觉图灵测试，在该测试中，法医和非法医人类志愿者（执行相同任务）的性能不及自动方案。