  This paper investigates the presence of overfitting when using Binary
Particle Swarm Optimization (BPSO) to perform the feature selection in a
context of Handwritten Signature Verification (HSV). SigNet is a state of the
art Deep CNN model for feature representation in the HSV context and contains
2048 dimensions. Some of these dimensions may include redundant information in
the dissimilarity representation space generated by the dichotomy
transformation (DT) used by the writer-independent (WI) approach. The analysis
is carried out on the GPDS-960 dataset. Experiments demonstrate that the
proposed method is able to control overfitting during the search for the most
discriminant representation.
