Fingerprint Liveness Detection from Single Image using Low Level Features and Shape Analysis

Fingerprint Liveness Detection from Single Image using Low Level Features and Shape Analysis
Title:
Fingerprint Liveness Detection from Single Image using Low Level Features and Shape Analysis
Other Titles:
IEEE Transactions on Information Forensics and Security
DOI:
10.1109/TIFS.2016.2535899
Publication Date:
01 July 2016
Citation:
R. K. Dubey, J. Goh and V. L. L. Thing, "Fingerprint Liveness Detection From Single Image Using Low-Level Features and Shape Analysis," in IEEE Transactions on Information Forensics and Security, vol. 11, no. 7, pp. 1461-1475, July 2016. doi: 10.1109/TIFS.2016.2535899
Abstract:
Fingerprint-based authentication systems have developed rapidly in the recent years. However, current fingerprint-based biometric systems are vulnerable to spoofing attacks. Moreover, single feature-based static approach does not perform equally over different fingerprint sensors and spoofing materials. In this paper, we propose a static software approach. We propose to combine low-level gradient features from speeded-up robust features, pyramid extension of the histograms of oriented gradient and texture features from Gabor wavelet using dynamic score level integration. We extract these features from a single fingerprint image to overcome the issues faced in dynamic software approaches, which require user cooperation and longer computational time. A experimental analysis done on LivDet 2011 data produced an average equal error rate (EER) of 3.95% over four databases. The result outperforms the existing best average EER of 9.625%. We also performed experiments with LivDet 2013 database and achieved an average classification error rate of 2.27% in comparison with 12.87% obtained by the LivDet 2013 competition winner.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISSN:
1556-6013
1556-6021
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