  SigNet is a state of the art model for feature representation used for
handwritten signature verification (HSV). This representation is based on a
Deep Convolutional Neural Network (DCNN) and contains 2048 dimensions. When
transposed to a dissimilarity space generated by the dichotomy transformation
(DT), related to the writer-independent (WI) approach, these features may
include redundant information. This paper investigates the presence of
overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the
feature selection in a wrapper mode. We proposed a method based on a global
validation strategy with an external archive to control overfitting during the
search for the most discriminant representation. Moreover, an investigation is
also carried out to evaluate the use of the selected features in a transfer
learning context. The analysis is carried out on a writer-independent approach
on the CEDAR, MCYT and GPDS datasets. The experimental results showed the
presence of overfitting when no validation is used during the optimization
process and the improvement when the global validation strategy with an
external archive is used. Also, the space generated after feature selection can
be used in a transfer learning context.
