Learning strategies for knowledge-base updating in online signature verification systems

Abstract

Updating of reference information is a crucial task for automatic signature verification. In fact, signature characteristics vary in time and whatever approach is considered the effectiveness of a signature verification system strongly depends on the extent to which reference information is able to model the changeable characteristics of users' signatures. This paper addresses the problem of knowledge-base updating in multi-expert signature verification systems and introduces a new strategy which exploits the collective behavior of classifiers to select the most profitable samples for knowledge-base updating. The experimental tests, carried out using the SUSig database, demonstrate the effectiveness of the new strategy. © 2013 Springer-Verlag.