Empirical Mode Decomposition Liveness Check in Fingerprint Time Series Captures

This work demonstrates a faster approach for liveness detection in fingerprint devices. The physiological phenomenon of perspiration, observed in time-series fingerprint images of live people, is used as a measure to classify ‘live’ fingers from ‘not live’ fingers. Pre-processing involves finding the singularity points using wavelets in the fingerprint images and transforming the information back in the spatial domain to form a spatial domain signal. Wavelet packet sieving is used to tune the modes so as to gain physical significance with reference to the evolving perspiration pattern in ‘live’ fingers. The percentage of energy contribution in the difference modes is used as a measure to differentiate live fingers from others. The proposed algorithm was applied to a data set of approximately 58 live, 50 spoof and 28 cadaver fingerprint images captured at 0 and after 2 sec, from three different types of scanners. An overall classification rate of 93.7% was achieved across all the three scanners.