An unsupervised 2D point-set registration algorithm for unlabeled feature points: Application to fingerprint matching

摘要

An unsupervised, iterative 2D point-set registration algorithm for unlabeled data and based on linear least squares is proposed, and subsequently utilized for minutia-based fingerprint matching. The matcher considers all possible minutia pairings and iteratively aligns the two sets until the number of minutia pairs does not exceed the maximum number of allowable one-to-one pairings. The first alignment establishes a region of overlap between the two minutia sets, which is then (iteratively) refined by each successive alignment. After each alignment, minutia pairs that exhibit weak correspondence are discarded. The process is repeated until the number of remaining pairs no longer exceeds the maximum number of allowable one-to-one pairings. The proposed algorithm is tested on both the FVC2000 and FVC2002 databases, and the results indicate that the proposed matcher is both effective and efficient for fingerprint authentication; it is fast and consciously utilizes as few computationally expensive mathematical functions (e.g. trigonometric, exponential) as possible. In addition to the proposed matcher, another contribution of the paper is the analytical derivation of the least squares solution for the optimal alignment parameters for two point-sets lacking one-to-one correspondence. (c) 2017 Elsevier B.V. All rights reserved.