Detecting Liveness in Fingerprint Scanners Using Wavelets: Results of the Test Dataset
A novel method is proposed to detect “liveness” associated with fingerprint devices. The physiological phenomenon of perspiration, observed only in live people, is used as a measure to classify ‘live’ fingers from ‘not live’ fingers. Pre-processing involves filtering of the images using different image processing techniques. Wavelet analysis of the images is performed using Daubechies wavelet. Multiresolution analysis is performed to extract information from the low frequency content, while wavelet packet analysis is performed to analyze the high frequency information content. A threshold is applied to the first difference of the information in all the sub-bands. The energy content of the changing wavelet coefficients, which are directly associated with the perspiration pattern, is used as a quantified measure to differentiate live fingers from others. The proposed algorithm was applied to a data set of approximately 30 live, 30 spoof and 14 cadaver fingerprint images from three different types of scanners. The algorithm was able to completely classify ‘live’ fingers from ‘not live’ fingers providing a method for improved spoof protection.
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- 1.Woodward, J.D., Orlans, N.M., Higgins, P.T.: Biometrics. McGraw-Hill, Osborne (2003)Google Scholar
- 2.Maltonie, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, New York (2003)Google Scholar
- 4.Schuckers, S.: Spoofing and anti-spoofing measures. In: Information Security Technical Report, vol. 7, pp. 56–62 (2002)Google Scholar
- 5.Matsumoto, T., Matsumoto, H., Yamada, K., Hoshino, S.: Impact of artificial ’gummy’ fingers on fingerprint systems. In: Proceedings of SPIE, January 2002, vol. 4677 (2002)Google Scholar
- 6.Putte, T., Keuning, J.: Biometrical fingerprint recognition: don’t get your fingers burned. In: Smart Card Research and Advanced Applications, pp. 289–303. Kluwer Academic Publisher, Dordrecht (2000)Google Scholar
- 7.Willis, D., Lee, M.: Biometrics under our thumb. Network computing (June 1998)Google Scholar
- 8.Derakshani, R., Schuckers, S., Hornak, L., Gorman, L.: Determination of vitality from a non-invasive biomedical measurement for use in fingerprint scanners. Pattern Recognition Journal 36(2) (2003)Google Scholar
- 9.Ruskai, M.B., Beylkin, G., Coifman, R., Daubechies, I., Mallat, S., Meyer, Y., Raphael, L.: Wavelet transform maxima and multiscale edges. In: Wavelets and Their Applications, Lowell, Massachusetts, pp. 67–104 (1992)Google Scholar
- 10.Daubechies, I.: Ten Lectures on Wavelets. Society of Industrial and Applied Mathematics (1998)Google Scholar
- 11.Ruskai, M.B., Beylkin, G., Coifman, R., Daubechies, I., Mallat, S., Meyer, Y., Raphael, L.: Size properties and wavelet packets. In: Wavelets and Their Applications, Lowell, Massachusetts, pp. 453–471 (1992)Google Scholar