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《Computer Science》 2014-12
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Fake Fingerprint Detection Algorithm Based on Curvelet Texture Analysis and SVM-KNN Classification

ZHANG Yong-liang;LIU Chao-fan;XIAO Gang;FANG Shan-shan;College of Computer Science & Technology,Zhejiang University of Technology;  
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.
【Fund】: 浙江省科技厅国际科技合作专项项目(2012C24009)资助
【CateGory Index】: TP391.41
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