Feature Selection and Binarization for On-Line Signature Recognition

The representation of a biometric trait through a set of parametric features is commonly employed in many biometric authentication systems. In order to avoid any loss of useful information, large sets of features have been defined for biometric characteristics such as signature, gait or face. However, the proposed sets often contain features which are irrelevant, correlated with other features, or even unreliable. In this paper we propose two different approaches for the selection of those features which guarantee the best recognition performances. Moreover, we also face the problem of the binary representation of the selected features. Specifically, an algorithm which selects the minimum number of bits which should be assigned to a given feature, in order to not affect the recognition performances, is here proposed. The effectiveness of the proposed approaches is tested considering a watermarking based on-line signature authentication system, and employing the public MCYT on-line signature corpus as experimental database.