A Novel Approach for Fingerprint Liveness Detection Using Gradient and Texture Features

Authors(2) :-P. Shanthi, R. Madhumathi

Fingerprints are good basis for individual identification by biometric authentication. Password based authentication systems are less secure than that of the fingerprint authentication where fingerprints and Iris are unique for every Individual. With the emerging use of biometric authentication systems in the past years, spoof fingerprint detection has become increasingly important. In this paper, we propose a static software approach that combines all sorts of fingerprint features. Initially, we extract the features of the fingerprint image using Gabor wavelet feature process. The extracted features are then aligned with histogram process. Each extracted features are preserved with dynamic score level integration. This dynamic approach consumes higher computational time. It has been experimented on the LivDet 2011 dataset which proves the efficiency of our proposed system. These have shown the classification rate of 9.625% with reduced error rate of 2.27%.

Authors and Affiliations

P. Shanthi
Research Scholar, Department of Computer Science, Sakthi college of Arts and Science For Women, oddanchatram, Tamil Nadu, India
R. Madhumathi
Assistant Professor, Department of Computer Science, Sakthi college of Arts and Science For Women, oddanchatram, Tamil Nadu, India

Fingerprint liveness, low level features, Gabor filters, texture analysis, Biometric Security.

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Publication Details

Published in : Volume 3 | Issue 3 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 400-408
Manuscript Number : CSEIT1833195
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

P. Shanthi, R. Madhumathi, "A Novel Approach for Fingerprint Liveness Detection Using Gradient and Texture Features", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.400-408, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT1833195

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