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Journal of Electrical and Computer Engineering
Volume 2018, Article ID 1539298, 9 pages
Research Article

Uniform Local Binary Pattern for Fingerprint Liveness Detection in the Gaussian Pyramid

College of Architecture and Artistic Design, Hunan Institute of Technology, Hengyang 421001, China

Correspondence should be addressed to Yujia Jiang; moc.361@daacaijuygnaij

Received 11 July 2017; Revised 2 October 2017; Accepted 15 November 2017; Published 9 January 2018

Academic Editor: William Sandham

Copyright © 2018 Yujia Jiang and Xin Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Fingerprint recognition schemas are widely used in our daily life, such as Door Security, Identification, and Phone Verification. However, the existing problem is that fingerprint recognition systems are easily tricked by fake fingerprints for collaboration. Therefore, designing a fingerprint liveness detection module in fingerprint recognition systems is necessary. To solve the above problem and discriminate true fingerprint from fake ones, a novel software-based liveness detection approach using uniform local binary pattern (ULBP) in spatial pyramid is applied to recognize fingerprint liveness in this paper. Firstly, preprocessing operation for each fingerprint is necessary. Then, to solve image rotation and scale invariance, three-layer spatial pyramids of fingerprints are introduced in this paper. Next, texture information for three layers spatial pyramids is described by using uniform local binary pattern to extract features of given fingerprints. The accuracy of our proposed method has been compared with several state-of-the-art methods in fingerprint liveness detection. Experiments based on standard databases, taken from Liveness Detection Competition 2013 composed of four different fingerprint sensors, have been carried out. Finally, classifier model based on extracted features is trained using SVM classifier. Experimental results present that our proposed method can achieve high recognition accuracy compared with other methods.