Handwritten signature is one of the most natural biometrics, the study of human physiological and behavioral patterns.
Behavioral biometrics includes signatures that may be different due to its owner gender or age because of intrinsic or
extrinsic factors. This paper presents the results of the author’s research on age and gender influence on verification
factors. The experiments in this research were conducted using a database that contains signatures and their associated
metadata. The used algorithm is based on the universal forgery feature idea, where the global classifier is able to classify
a signature as a genuine one or, as a forgery, without the actual knowledge of the signature template and its owner.
Additionally, the reduction of the dimensionality with the MRMR method is discussed.
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