Fake-fingerprint detection using multiple static features

Heeseung Choi, Raechoong Kang, Kyoungtaek Choi, Andrew Teoh Beng Jin, Jaihie Kim

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

Recently, fake fingerprints have become a serious concern for the use of fingerprint recognition systems. We introduce a novel fakefingerprint 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.

Original languageEnglish
Article number047202
JournalOptical Engineering
Volume48
Issue number4
DOIs
Publication statusPublished - 2009 Apr 20

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All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Engineering(all)

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