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http://dx.doi.org/10.13089/JKIISC.2017.27.1.39

Fingerprint Liveness Detection Using Patch-Based Convolutional Neural Networks  

Park, Eunsoo (Inha University)
Kim, Weonjin (Inha University)
Li, Qiongxiu (Inha University)
Kim, Jungmin (Visionin Inc.)
Kim, Hakil (Inha University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.27, no.1, 2017 , pp. 39-47 More about this Journal
Abstract
Nowadays, there have been an increasing number of illegal use cases where people try to fabricate the working hours by using fake fingerprints. So, the fingerprint liveness detection techniques have been actively studied and widely demanded in various applications. This paper proposes a new method to detect fake fingerprints using CNN (Convolutional Neural Ntworks) based on the patches of fingerprint images. Fingerprint image is divided into small square sized patches and each patch is classified as live, fake, or background by the CNN. Finally, the fingerprint image is classified into either live or fake based on the voting result between the numbers of fake and live patches. The proposed method does not need preprocessing steps such as segmentation because it includes the background class in the patch classification. This method shows promising results of 3.06% average classification errors on LivDet2011, LivDet2013 and LivDet2015 dataset.
Keywords
fingerprint liveness detection; CNN; fake fingerprint detection; presentation attack;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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