Learning Global Fingerprint Features by Training a Fully Convolutional Network with Local Patches

Abstract

Learning fingerprint representations is of critical importance in fingerprint indexing algorithms. Convolutional neural networks (CNNs) provide fingerprint features that perform remarkably well. In previous CNN based methods, global fingerprint features are acquired by training with entire fingerprints or by aggregating local descriptors. The former method does not make full use of the information of matched minutiae, thereby achieving relatively-low performance. While the latter way needs to extract all local features, which is time-consuming. In this paper, we propose an efficient strategy to learn global features making full use of the information of matched minutiae. We train a fully convolutional network (FCN) with local patches. Patch classes contain more information than the original fingerprint classes, and such information is helpful to learn discriminative features. In the indexing stage, we utilize the capability of FCN to get global features of whole fingerprints. Furthermore, the learned features are robust to translation, rotation, and occlusion. Therefore, we do not need to align fingerprints. The proposed approach outperforms the state-of-the-art on benchmark datasets. We achieve 99.83% identification accuracy at the penetration rate of 1% using only 256-bytes per fingerprint on NIST SD4.