Performance and robustness: A trade-off in dynamic signature verification

A performance and robustness study for on-line signature verification is presented. Experiments are carried out on the MCYT database comprising 16,500 signatures from 330 subjects, which are parameterized by means of a 100-feature set which can be divided into four different groups according to the signature information they contain, namely: i) time, ii) speed and acceleration, iii) direction, and iv) geometry. The SFFS feature selection algorithm is used to search for the best performing feature subsets under the skilled and random forgeries scenarios, and to find the most robust subsets against a hill-climbing attack. Comparative experiments are given, where it is shown that the most discriminant parameters are those regarding geometry information, while the most robust are the time related features.