On Dissimilarity Representation and Transfer Learning for Offline Handwritten Signature Verification

When compared to Writer-Dependent (WD) Handwritten Signature Verification, in which a model is trained for each individual writer, the Writer-Independent (WI) approach offers greater scalability, since only a single model is trained for all users from a dissimilarity space generated by the dichotomy transformation. However, many samples from the dissimilarity space are redundant and have little influence during the training of the verification model. This work investigates whether prototype selection (PS) preprocessing can be used in the space resulting from the dichotomy transformation without degrading the performance of the classifier. Furthermore, an investigation is also performed to examine the use of a WI classifier in a transfer learning scenario, i.e., where the classifier is trained in one dataset, and is used to verify signatures in other datasets. The experiments reported herein show that the use of prototype selection in the dissimilarity space allows a reduction in the complexity of the classifier without degrading its generalization performance. In addition, the results show that the WI classifier is scalable enough to be used in a transfer learning approach, with a resulting performance comparable to that of a classifier trained and tested in the same dataset. An analysis of the results obtained based on the instance hardness (IH) measure and dendrogram diagrams is performed in order to better understand the behavior of the resulting dichotomy transformation.