Paper Info
Title
Joint Estimation Of Pose And Singular Points Of Fingerprints
Abstract
Fingerprint pose estimation is a challenging problem since the pose is not defined by salient anatomical features and fingerprint images usually suffer from noise and small area. In this article, we proposed a method for joint estimation of pose and singular points of fingerprints, with the expectation that the pose and singular points can improve each other. By virtue of that singular points can be located accurately, we hope to improve the accuracy of pose estimation. Meanwhile, the robustness of pose estimation can improve the anti-noise performance of singular point detection. To achieve this, we propose a multi-task deep neural network, which contains a feature extraction body and two estimation heads for singular point and pose respectively. The proposed network can deal with various types of fingerprints, including plain, rolled and latent fingerprints. Experiments on four databases (NIST SD4, SD14, SD27 and FVC2004 DB1A) show that (1) the estimated poses and detected singular points are close to manual annotations despite of different image qualities; (2) the estimated poses for mated fingerprint pairs are consistent; and (3) the proposed pose estimation method outperforms state-of-the-art methods while utilized as pose constraint for a fingerprint indexing algorithm.
Year
DOI
Venue
2021
10.1109/TIFS.2020.3036803
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
Keywords
DocType
Volume
Feature extraction, Pose estimation, NIST, Indexing, Fingerprint recognition, pose estimation, singular points detection, deep neural networks, multi-task learning
Journal
16
ISSN
Citations 
PageRank 
1556-6013
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Qihao Yin100.34
Jianjiang Feng281462.59
Jiwen Lu33105153.88
Jie Zhou42103190.17