Successive minutia‐free mosaicking for small‐sized fingerprint recognition

Abstract

Small-sized fingerprint sensors, due to the convenience of integration, are widely used in many applications, especially on smart phones. However, the friction ridge information decreases with the reduction of the collected fingerprint area, resulting in degraded recognition performance. Mosaicking fingerprint impressions has been proved to be effective in boosting the recognition accuracy. Nonetheless, the minutiae-based mosaicking methods do not work well when there is no sufficient number of minutiae in the overlapping area while existing minutia-free mosaicking methods are not robust to distortion and result in low mosaicking accuracy. In this study, a novel minutia-free mosaicking algorithm used the coarse-to-fine approach is proposed to obtain a larger fingerprint impression from a couple of small-sized fingerprint impressions. It consists of three stages: an orientation field-based coarse alignment, a ridge matching-based fine alignment, and a nonlinear deformation correction with block-correspondence Thin Plate Spline model. Experimental results on the XDfinger database demonstrate that the proposed method outperforms the other six mosaicking methods in terms of reject-to-fuse rate, registration accuracy, and verification performance. Specifically, in the verification scenario, the equal error rate is reduced from 1.98% of a single impression to 0.41% of two impressions mosaicked by the authors' method.