Capturing large intra-class variations of biometric data by template co-updating

The representativeness of a biometric template gallery to the novel data has been recently faced by proposing ldquotemplate updaterdquo algorithms that update the enrolled templates in order to capture, and represent better, the subjectpsilas intra-class variations. Majority of the proposed approaches have adopted ldquoselfrdquo update technique, in which the system updates itself using its own knowledge. Recently an approach named template co-update, using two complementary biometrics to ldquoco-updaterdquo each other, has been introduced. In this paper, we investigate if template co-update is able to capture intra-class variations better than those captured by state of art self update algorithms. Accordingly, experiments are conducted under two conditions, i.e., a controlled and an uncontrolled environment. Reported results show that co-update can outperform ldquoselfrdquo update technique, when initial enrolled templates are poor representative of the novel data (uncontrolled environment), whilst almost similar performances are obtained when initial enrolled templates well represent the input data (controlled environment).