Improving BPSO-based feature selection applied to offline WI handwritten signature verification through overfitting control

Improving BPSO-based feature selection applied to offline WI handwritten signature verification through overfitting control

Souza, Victor L.F. and Oliveira, Adriano L.I. and Cruz, Rafael M.O. and Sabourin, Robert

arXiv 2020

Abstract : 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.