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This paper describes an image-based system to detect spoof fingerprint attacks in fingerprint biometric systems. It is based on the observation that, real and spoof fingerprints exhibit different textural characteristics. These are based on structural, orientation, roughness, smoothness and regularity differences of diverse regions in a fingerprint image. Local binary pattern (LBP) histograms are used to capture these textural details. Wavelet energy features characterizing ridge frequency and orientation information are also used for improving the efficiency of the proposed method. Dimensionality of the integrated feature set is reduced by running Pudilpsilas Sequential Forward Floating Selection (SFFS) algorithm. We propose to use a hybrid classifier, formed by fusing three classifiers: neural network, support vector machine and k-nearest neighbor using the ldquoProduct Rulerdquo. Classification rates achieved with these classifiers, including a hybrid classifier are in the range ~94% to ~97%. Experimental results indicate that, the new liveness detection approach is a very promising technique, as it needs only one fingerprint and no extra hardware to detect vitality.