Singular points detection with semantic segmentation networks

TL;DR

This paper introduces SinNet, a deep learning-based semantic segmentation approach for detecting singular points in fingerprints, achieving significant improvements over existing methods especially on low-quality images.

Contribution

The paper presents SinNet, a novel CNN for fingerprint singular point detection that requires less data and outperforms traditional and deep learning methods.

Findings

11% increase in correctly detected fingerprints

Over 18% improvement in core detection rate

Superior performance on SPD2010 dataset

Abstract

Singular points detection is one of the most classical and important problem in the field of fingerprint recognition. However, current detection rates of singular points are still unsatisfactory, especially for low-quality fingerprints. Compared with traditional image processing-based detection methods, methods based on deep learning only need the original fingerprint image but not the fingerprint orientation field. In this paper, different from other detection methods based on deep learning, we treat singular points detection as a semantic segmentation problem and just use few data for training. Furthermore, we propose a new convolutional neural network called SinNet to extract the singular regions of interest and then use a blob detection method called SimpleBlobDetector to locate the singular points. The experiments are carried out on the test dataset from SPD2010, and the proposed method has much better performance than the other advanced methods in most aspects. Compared with the state-of-art algorithms in SPD2010, our method achieves an increase of 11% in the percentage of correctly detected fingerprints and an increase of more than 18% in the core detection rate.