Super-template Generation Using Successive Bayesian Estimation for Fingerprint Enrollment

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Abstract

This paper proposes an algorithm for generating a super-template from multiple fingerprint impressions in fingerprint enrollment for the purpose of increasing recognition accuracy. The super-template is considered as a single fingerprint template which contains highly likely true minutiae based on multiple fingerprint images. The proposed algorithm creates the super-template, in which the credibility of each minutia is updated by applying a successive Bayesian estimation (SBE) to a sequence of templates obtained from input fingerprint images. Consequently, the SBE assigns a higher credibility to frequently detected minutiae and a lower credibility to minutiae that are rarely found from the input templates. Likewise, the SBE is able to estimate the credibility of the minutia type (ridge ending or bifurcation). Preliminary experiments demonstrate that, as the number of fingerprint images increases, the proposed algorithm can improve the recognition performance, while keeping the processing time and memory storage required for the super-template almost constant.