Next Article in Journal
A New Ant Colony-Based Methodology for Disaster Relief
Next Article in Special Issue
Near-Duplicate Image Detection System Using Coarse-to-Fine Matching Scheme Based on Global and Local CNN Features
Previous Article in Journal
Dynamics of General Class of Difference Equations and Population Model with Two Age Classes
Previous Article in Special Issue
A Novel Hierarchical Secret Image Sharing Scheme with Multi-Group Joint Management
Open AccessArticle

Fingerprint Liveness Detection Based on Fine-Grained Feature Fusion for Intelligent Devices

1
College of Management and Economics, Tianjin University, Tianjin 300072, China
2
School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(4), 517; https://doi.org/10.3390/math8040517
Received: 1 March 2020 / Revised: 26 March 2020 / Accepted: 27 March 2020 / Published: 3 April 2020
(This article belongs to the Special Issue Computing Methods in Steganography and Multimedia Security)
Currently, intelligent devices with fingerprint identification are widely deployed in our daily life. However, they are vulnerable to attack by fake fingerprints made of special materials. To elevate the security of these intelligent devices, many fingerprint liveness detection (FLD) algorithms have been explored. In this paper, we propose a novel detection structure to discriminate genuine or fake fingerprints. First, to describe the subtle differences between them and take advantage of texture descriptors, three types of different fine-grained texture feature extraction algorithms are used. Next, we develop a feature fusion rule, including five operations, to better integrate the above features. Finally, those fused features are fed into a support vector machine (SVM) classifier for subsequent classification. Data analysis on three standard fingerprint datasets indicates that the performance of our method outperforms other FLD methods proposed in recent literature. Moreover, data analysis results of blind materials are also reported. View Full-Text
Keywords: fingerprint liveness detection; feature fusion; texture descriptor; SVM fingerprint liveness detection; feature fusion; texture descriptor; SVM
Show Figures

Figure 1

MDPI and ACS Style

Li, X.; Cheng, W.; Yuan, C.; Gu, W.; Yang, B.; Cui, Q. Fingerprint Liveness Detection Based on Fine-Grained Feature Fusion for Intelligent Devices. Mathematics 2020, 8, 517.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop