access icon free Fingerprint liveness detection using local texture features

© The Institution of Engineering and Technology
Received 20/01/2016, Accepted 06/12/2016, Revised 27/10/2016, Published 08/12/2016

The problem of fingerprint liveness detection has received an increasing attention in the last decade, as attested by the organisation of three editions of an international competition, named LivDet, dedicated to this challenge. LivDet editions and other works in the literature showed that the performance of current fingerprint liveness detection algorithms is not good enough to allow empowering a fingerprint verification system with a module aimed to distinguish alive from fake fingerprint images. However, recent developments have shown that texture-based features can provide promising solutions to this problem. In this study, a novel fingerprint liveness descriptor named binarised statistical image features (BSIFs) is adopted. Similarly to local binary pattern and local phase quantisation-based representations, BSIF encodes the local fingerprint texture into a feature vector by using a set of filters that, unlike other methods, are learnt from natural images. Extensive experiments with over 40,000 live and fake fingerprint images show that the authors’ proposed method outperforms most of the state-of-the-art algorithms, allowing a step ahead to the real integration of fingerprint liveness detectors into verification systems.

Inspec keywords: statistical analysis; image texture; fingerprint identification

Other keywords: fingerprint liveness detection; fake fingerprint images; local texture features; local binary pattern; local phase quantisation-based representations; LivDet; fingerprint verification system; binarised statistical image features

Subjects: Computer vision and image processing techniques; Other topics in statistics; Image recognition; Other topics in statistics

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