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http://dx.doi.org/10.13089/JKIISC.2016.26.5.1259

Fingerprint Liveness Detection and Visualization Using Convolutional Neural Networks Feature  

Kim, Weon-jin (Inha University)
Li, Qiong-xiu (Inha University)
Park, Eun-soo (Inha University)
Kim, Jung-min (Visionin Inc.)
Kim, Hak-il (Inha University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.26, no.5, 2016 , pp. 1259-1267 More about this Journal
Abstract
With the growing use of fingerprint authentication systems in recent years, the fake fingerprint detection is becoming more and more important. This paper mainly proposes a method for fake fingerprint detection based on CNN, it will visualize the distinctive part of detected fingerprint which provides a deeper insight in CNN model. After the preprocessing part using fingerprint segmentation, the pretrained CNN model is used for detecting the liveness detection. Not only a liveness detection but also feature analysis about the live fingerprint and fake fingerprint are provided after classifying which materials are used for making the fake fingerprint. Our system is evaluated on three databases in LivDet2013, which compromise almost 6500 live fingerprint images and 6000 fake fingerprint images in total. The proposed method achieves 3.1% ACE value about the liveness detection and achieves 79.58% accuracy on LiveDet2013.
Keywords
fingerprint liveness detection; CNN; visualization;
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