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Optical Engineering

Fake-fingerprint detection using multiple static features
Author(s): Heeseung Choi; Raechoong Kang; Kyoungtaek Choi; Andrew Teoh Beng Jin; Jai Hie Kim
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Paper Abstract

Recently, fake fingerprints have become a serious concern for the use of fingerprint recognition systems. We introduce a novel fake-fingerprint detection method that uses multiple static features. With regard to the usability of the method for field applications, we employ static features extracted from one image to determine the aliveness of fingerprints. We consider the power spectrum, histogram, directional contrast, ridge thickness, and ridge signal of each fingerprint image as representative static features. Each feature is analyzed with respect to the physiological and statistical distinctiveness of live and fake fingerprints. These features form a feature vector set and are fused at the feature level through a support vector machine classifier. For performance evaluation and comparison, a total of 7200 live images and 9000 fake images were collected using four sensors (three optical and one capacitive). Experimental results showed that proposed method achieved approximately 1.6% equal-error rate with optical-based sensors. In the case of the capacitive sensor, there was no test error when only one image was used for a decision. Based on these results, we conclude that the proposed method is a simple yet promising fake-fingerprint inspection technique in practice.

Paper Details

Date Published: 1 April 2009
PDF: 13 pages
Opt. Eng. 48(4) 047202 doi: 10.1117/1.3114606
Published in: Optical Engineering Volume 48, Issue 4
Show Author Affiliations
Heeseung Choi, Yonsei Univ. (Korea, Republic of)
Raechoong Kang, Yonsei Univ. (Korea, Republic of)
Kyoungtaek Choi, Yonsei Univ. (Korea, Republic of)
Andrew Teoh Beng Jin, Yonsei Univ. (Korea, Republic of)
Jai Hie Kim, Yonsei Univ. (Korea, Republic of)


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