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.

脱机手写签名验证（HSV）问题面临的挑战和困难包括大量的作者，具有较高的类内变异性和每个类分配严重不足的每个样本培训样本。解决这些问题的一个不错的选择是使用独立于作者的（WI）框架。在WI系统中，训练了一个模型来从二分法转换生成的相异空间为所有作者执行签名验证。该框架的优点之一是可扩展性以应对其中的一些挑战，并且易于管理新作者，因此可用于迁移学习环境。在这项工作中，我们对这种方法进行了白盒分析，重点介绍了该方法如何应对挑战，通过融合功能动态选择参考，及其在迁移学习中的应用。使用实例硬度（IH）量度在实例级别执行所有分析。实验结果表明，使用IH分析，我们能够鉴定出“好”和“差”质量的熟练伪造，以及正负样本之间的前沿区域。通过考虑这些特征，可以对未来的调查方法进行改进，以改善真实签名与熟练伪造之间的区别。