Affiliation:
1. Insituto Tecnológico de Aguascalientes, Mexico
2. Universidad de Guadalajara, Mexico
Abstract
This chapter presents the use of multi-objective optimization for on-line automatic verification of handwritten signatures; as discriminating features of each signer are used here some functions of time and space of the position of the pen on the paper; these functions are directly used in a multi-objective optimization task in order to obtain high values of false positives indicators (FAR False Acceptance Rate) and false negatives (FFR, false rejection rate). The genetic algorithms are used to create a signer´s model that optimally characterizes him, thus rejecting the skilled forgeries and recognizing genuine signatures with large variation with respect to the training set.
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