B. RajeshKumar, M. Manimekalai, M. Sanjay, P. V. Bharath raj, RVS College of Engineering and Technology, Coimbatore, Tamilnadu, India.
DOI : 01.0401/ijaict.2017.03.07
International Journal of Advanced Information and Communication Technology
Received On : September 16, 2019
Revised On : October 12, 2019
Accepted On : November 20, 2019
Published On : December 05, 2019
Volume 06, Issue 12
Pages : 1206-1209
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.
Keywords
Fingerprint liveness, low level features, Gabor filters./p>
Cite this article
B. RajeshKumar, M. Manimekalai, M. Sanjay, P. V. Bharath raj, “Espying the Live Fingerprint from Single Image using Low Level Features and Shape Analysis” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp.1206-1209, December 05, 2019.
Copyright
© 2019 B. RajeshKumar, M. Manimekalai, M. Sanjay, P. V. Bharath raj. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.