We propose an image-based method using Contourlet transform [5] to detect liveness in fingerprint biometric systems. We observe that real and spoof fingerprint images exhibit different textural characteristics. Wavelet transform although widely used for liveness detection is not the ideal one. Wavelets are not very effective in representing images containing lines and contours [5]. Recent Contourlet transform allows representing contours in a more efficient way than the wavelets [5]. Fingerprint is made of only contours of ridges; hence Contourlet transform is more suitable for fingerprint processing than the wavelets. Therefore, we use Contourlet energy and co-occurrence signatures to capture textural intricacies of images. After downsizing features with Plus l – take away r method, we test them on various classifiers: logistic regression, support vector machine and AdTree using our databases consisting of 185 real, 90 Fun-Doh (Play-Doh) and 150 Gummy fingerprint images. We then select the best classifier and use at as a base classifier to form an ensemble classifier obtained by fusing a stack of “K” base classifiers using the “Majority Voting Rule” (i.e. bagging). Experimental results indicate that, the new liveness detection approach is very promising as it needs only one fingerprint and no extra hardware to detect vitality.