Local binary pattern and wavelet-based spoof fingerprint detection
by Shankar Bhausaheb Nikam, Suneeta Agarwal
International Journal of Biometrics (IJBM), Vol. 1, No. 2, 2008

Abstract: Perspiration phenomenon is very significant to detect liveness of a finger. It requires two consecutive fingerprints to notice perspiration and therefore the method is slow. Some other methods in the literature need extra hardware to detect liveness. To alleviate these problems, in this paper, a new texture-based method which needs only one fingerprint is proposed. It is based on the observation that, real and spoof fingerprints exhibit different textural characteristics. Local binary pattern (LBP) histograms are used to capture these textural details. Wavelet energy features characterising ridge frequency and orientation information are also used for improving the efficiency of the proposed method. Dimensionalities of the feature sets are reduced by running sequential forward floating selection (SFFS). LBP features and wavelet energy features are independently tested on various classifiers: AdaBoost.M1, support vector machine and k-nearest neighbour. Features are also tested on a hybrid classifier formed by fusing all the mentioned earlier classifiers by the 'majority voting rule'. Fingerprint databases consisting of 185 real, 90 fun-doh and 150 gummy fingerprints are created. Experimental results indicate that, the performance of new liveness detection approach is very promising, as it needs only one fingerprint and no extra hardware to detect vitality.

Online publication date: Sat, 30-Aug-2008

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