Fast minutiae extractor using neural network

Abstract

Abstract In this paper, we propose a fast and reliable neural network-based algorithm for fingerprint minutiae extraction. In particular, our algorithm involves a two-stage process: in the first stage, a network generates candidate patches in which minutiae may exist; in the second stage, another network extracts minutiae from every patch.These two networks share a common part to reduce the running time. Moreover, we analyze the properties of fingerprint images and propose a principle for designing efficient networks for minutiae extraction. For efficiency, our algorithm extracts minutiae directly from raw fingerprint images, without traditional pre-processes. Another benefit of this design is that the networks only require datasets with minutiae labels for training. On the public fingerprint datasets (FVC 2002 and 2004), our algorithm requires 26 ms on average to extract minutiae from one fingerprint on a single GPU. Compared with other neural network-based algorithms, our algorithm runs approximately 10 times faster and does not lose substantial accuracy.