Template update in biometric recognition system is aimed to improve the representativeness of available templates in order to make them adaptive to the large intra-class variations characterizing biometrics (e.g. fingerprints and faces). Among others, semi-supervised approaches to template update have been recently proposed. Since the lack of representativeness is due to the impossibility of sampling all possible variations of a given client biometric, these approaches exploit samples submitted during the recognition phase by adding the “highly genuine” ones to the related client gallery. In particular, the template co-update algorithm, which uses the mutual help of two complementary biometric matchers, has shown promising experimental results. However, no theoretical model has been proposed to explain the behaviour of the co-update algorithm and support the experimental results. This is the goal of this paper. Experimental results show the correctness of the proposed theoretical model.
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