Fingerprint Liveness Detection Based on Multi-Scale LPQ and PCA

作     者：Chengsheng Yuan Xingming Sun Rui Lv 

作者机构：School of Computer and SoftwareNanjing University of Information Science & Technology Jiangsu Engineering Center of Network MonitoringNanjing University of Information Science and Technology 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2016年第13卷第7期

页      面：60-65页

核心收录：

学科分类：090801[农学-水产养殖] 0810[工学-信息与通信工程] 0808[工学-电气工程] 0908[农学-水产] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0839[工学-网络空间安全] 09[农学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the NSFC (U1536206,61232016,U1405254,61373133, 61502242) BK20150925 the PAPD fund 

主　　题：指纹特征 活体检测 多尺度 PCA 指纹认证系统 支持向量机分类器 多分辨率分析 用户身份 

摘      要：Fingerprint authentication system is used to verify users identification according to the characteristics of their fingerprints.However,this system has some security and privacy problems.For example,some artificial fingerprints can trick the fingerprint authentication system and access information using real users identification.Therefore,a fingerprint liveness detection algorithm needs to be designed to prevent illegal users from accessing privacy information.In this paper,a new software-based liveness detection approach using multi-scale local phase quantity(LPQ) and principal component analysis(PCA) is proposed.The feature vectors of a fingerprint are constructed through multi-scale LPQ.PCA technology is also introduced to reduce the dimensionality of the feature vectors and gain more effective features.Finally,a training model is gained using support vector machine classifier,and the liveness of a fingerprint is detected on the basis of the training model.Experimental results demonstrate that our proposed method can detect the liveness of users fingerprints and achieve high recognition accuracy.This study also confirms that multi-resolution analysis is a useful method for texture feature extraction during fingerprint liveness detection.