The heart is potentially a highly secured biometric modality. Although many templates have been proposed to be extracted from heart-signal for biometric authentication, they have yet to reach a single digit equal error rate (EER) of false matches and false non-matches when applied on large across-session data sets, where gallery and probe data are taken from different sessions. However, since different templates possess different strengths, the fusion of them has a great potential to improve the authentication performance. We propose an efficient template selection algorithm to select a suitable subset of templates from a given set to obtain a minimal EER. The fusion of the subset of templates selected by this algorithm from a set of seven state-of-the-art templates has obtained a significant 5% reduction of EER in authentication in our experiments on a large database of finger-based ECG signals captured in two different sessions.