Semi-supervised co-update of multiple matchers

Classification algorithms based on template matching are used in many applications (e.g., face recognition). Performances of template matching classifiers are obviously affected by the representativeness of available templates. In many real applications, such representativeness can substantially decrease over the time (e.g., due to “aging” effects in biometric applications). Among algorithms which have been recently proposed to deal with such issue, the template co-update algorithm uses the mutual help of two complementary template matchers to update the templates over the time in a semi-supervised way [8]. However, it can be shown that the template co-update algorithm derives from a more general framework which supports the use of more than two template matching classifiers. The aim of this paper is to point out this fact and propose the co-update of multiple matchers. Preliminary experimental results are shown to validate the proposed model.