A biometric system provides poor performances when the input data exhibit intra-class variations which are not well represented by the enrolled template set. This problem has been recently faced by template update techniques. The majority of the proposed techniques can be regarded as "self-update" methods, as the system updates its own templates using the recognition results provided by the same templates. However, this approach can only exploit the input data "near" to the current templates resulting in "local" template optimization, namely, only input samples very similar to the current templates are exploited for update. To address this issue, this paper proposes a "global" optimization of templates based on the graph mincut algorithm. The proposed approach can update templates by analysing the underlying structure of input data collected during the system's operation. This is achieved by a graph drawn using a pair-wise similarity measure between enrolled and input data. Investigation of this novel template update technique has been done by its application to face verification, as a case study. The reported results show the effectiveness of the proposed technique in comparison to state of art self-update techniques.
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