Abstract: Fake fingerprint attack,as a kind of simple and practical way of cracking fingerprint identification,is used by some outlaws.The current mainstream method of fake fingerprint detection is texture analysis,but the original texture analysis doesn’t include coarseness analysis caused by the difference between the materials of fake fingerprint and real fingerprint.In this paper,a novel method was proposed based on the curvelet transform and image texture feature with support vector machine and K-nearest neighbor(SVM-KNN) classification.Firstly,curvelet coefficient features with different scales and directions are extracted.Secondly,texture features are extracted from first order statistics,gray level co-occurrence matrix(GLCM) and Markov random field(MRF) in curvelet reconstructed image,and then fingerprint images are trained to obtain the classification criterion by SVM.Lastly,SVM-KNN classification is used for fake fingerprint detection.The experimental results in the databases of the Liveness Detection Competition 2011 (LivDet2011) show that the proposed method is effective and superior.

Key words: Curvelet transform,Gray level co-occurrence matrix,Markov random field,SVM-KNN