High number of writers, small number of training samples per writer with high intra-class variability and heavily imbalanced class distributions are among the challenges and difficulties of the offline Handwritten Signature Verification (HSV) problem. A good alternative to tackle these issues is to use a writer-independent (WI) framework. In WI systems, a single model is trained to perform signature verification for all writers from a dissimilarity space generated by the dichotomy transformation. Among the advantages of this framework is its scalability to deal with some of these challenges and its ease in managing new writers, and hence of being used in a transfer learning context. In this work, we present a white-box analysis of this approach highlighting how it handles the challenges, the dynamic selection of references through fusion function, and its application for transfer learning. All the analyses are carried out at the instance level using the instance hardness (IH) measure. The experimental results show that, using the IH analysis, we were able to characterize "good" and "bad" quality skilled forgeries as well as the frontier region between positive and negative samples. This enables futures investigations on methods for improving discrimination between genuine signatures and skilled forgeries by considering these characterizations.

中文翻译：

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用于离线手写签名验证的作者独立二分变换的白盒分析

大量的作者、每个作者的训练样本数量少、类内可变性高和类分布严重不平衡是离线手写签名验证 (HSV) 问题的挑战和困难之一。解决这些问题的一个很好的替代方法是使用独立于编写器 (WI) 的框架。在 WI 系统中，训练单个模型以对来自二分变换生成的不同空间的所有作者执行签名验证。该框架的优势之一是其可扩展性以应对其中一些挑战，并且易于管理新作者，因此可以在迁移学习环境中使用。在这项工作中，我们对这种方法进行了白盒分析，重点介绍了它如何应对挑战，通过融合函数动态选择参考，及其在迁移学习中的应用。所有分析均使用实例硬度 (IH) 度量在实例级别进行。实验结果表明，使用 IH 分析，我们能够表征“好”和“坏”质量的熟练伪造以及正样本和负样本之间的边界区域。通过考虑这些特征，这使得未来能够对提高真实签名和技术伪造之间的区分的方法进行调查。质量熟练的伪造以及正负样本之间的边界区域。通过考虑这些特征，这使得未来能够对提高真实签名和技术伪造之间的区分的方法进行调查。质量熟练的伪造以及正负样本之间的边界区域。通过考虑这些特征，这使得未来能够对提高真实签名和技术伪造之间的区分的方法进行调查。