  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.
