Extraction of Fingerprint Pore Using Convolutional Neural Networks

Abstract

Sweat pores have been recently employed for automated fingerprint recognition, in which the pores are usually extracted by using a convolutional neural networks. In this paper, however, we show that real pores are not always isotropic. To accurately and robustly extract pores, we propose an adaptive anisotropic pore model, whose parameters are adjusted adaptively according to the fingerprint ridge direction and period. The fingerprint image is partitioned into blocks and a local pore model is determined for each block. With the local pore model, a matched filter is used to extract the pores within each block. Experiments on a high resolution fingerprint dataset are performed and the results demonstrate that the proposed pore model and pore extraction method can locate pores more accurately and robustly in comparison with other state-of the-art pore extractors.