Multi-scaling Detection of Singular Points Based, on Fully Convolutional Networks in Fingerprint Images

Abstract

Most of the existing conventional methods for singular points detection of fingerprints depend on the orientation fields of fingerprints, which cannot achieve the reliable and accurate detection of poor quality fingerprints. In this paper, a novel algorithm is proposed for fingerprint singular points detection, which combines multi-scaling fully convolutional networks (FCN) and probability model. Firstly, we divide fingerprint image into overlapping blocks and pose them into a classification problem. And we propose a convolutional neural network (ConvNet) based approach for estimating whether the center of a block is one singularity point. Then, we transform the ConvNet into FCN and fine-tuned. Finally, we adopt probabilistic methods to determine the actual positions of singular points. The performance testing was conducted on NIST DB4 and FVC2002 DB1 database, which concluded that the proposed algorithm gives better results than competing approaches.