An anti-spoofing technique using multiple textural features in fingerprint scanners

Fingerprint verification systems may be circumvented by fake fingerprints produced using inexpensive materials like gelatin or silicon. An efficient countermeasure against these attacks is given by liveness detection. In the recent literature, different algorithms for detecting signs of vitality have been proposed. The cheapest techniques are software-based and utilize acquired fingerprint images in order to extract static or dynamic characteristics. In this paper, we propose a novel software-based solution for liveness detection based on static features coming out from the visual texture of the image. The reported results show that the use of our features effectively improves the discriminative power (between live and fake fingerprints) achieved by the algorithms proposed during the Liveness Detection Competition 2009.

[1]  Stephanie Schuckers,et al.  Determination of vitality from a non-invasive biomedical measurement for use in fingerprint scanners , 2003, Pattern Recognit..

[2]  Suneeta Agarwal,et al.  Curvelet-based fingerprint anti-spoofing , 2010, Signal Image Video Process..

[3]  Stephanie Schuckers,et al.  A wavelet-based approach to detecting liveness in fingerprint scanners , 2004, SPIE Defense + Commercial Sensing.

[4]  Hakil Kim,et al.  Liveness Detection of Fingerprint Based on Band-Selective Fourier Spectrum , 2007, ICISC.

[5]  Satoshi Hoshino,et al.  Impact of artificial "gummy" fingers on fingerprint systems , 2002, IS&T/SPIE Electronic Imaging.

[6]  Jaihie Kim,et al.  Aliveness Detection of Fingerprints using Multiple Static Features , 2007 .

[7]  Stephanie Schuckers,et al.  Integrating a wavelet based perspiration liveness check with fingerprint recognition , 2009, Pattern Recognit..

[8]  Alessandra Lumini,et al.  An evaluation of direct attacks using fake fingers generated from ISO templates , 2010, Pattern Recognit. Lett..

[9]  Anil K. Jain,et al.  Texture Analysis , 2018, Handbook of Image Processing and Computer Vision.

[10]  Béla Julesz,et al.  Visual Pattern Discrimination , 1962, IRE Trans. Inf. Theory.

[11]  Gian Luca Marcialis,et al.  Analysis and Selection of Features for the Fingerprint Vitality Detection , 2006, SSPR/SPR.

[12]  Y. S. Moon,et al.  Wavelet based fingerprint liveness detection , 2005 .

[13]  Gian Luca Marcialis,et al.  First International Fingerprint Liveness Detection Competition - LivDet 2009 , 2009, ICIAP.

[14]  Stephanie Schuckers,et al.  Fingerprint Liveness Detection Using Local Ridge Frequencies and Multiresolution Texture Analysis Techniques , 2006, 2006 International Conference on Image Processing.

[15]  Julian Fierrez,et al.  Vulnerabilities in biometric systems: Attacks and recent advances in liveness detection , 2007 .

[16]  Stephanie Schuckers,et al.  Detecting Liveness in Fingerprint Scanners Using Wavelets: Results of the Test Dataset , 2004, ECCV Workshop BioAW.

[17]  J. Fierrez-Aguilar,et al.  On the Vulnerability of Fingerprint Verification Systems to Fake Fingerprints Attacks , 2006, Proceedings 40th Annual 2006 International Carnahan Conference on Security Technology.