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 artifici...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.展开更多
The case–cohort design has been widely used to reduce the cost of covariate measurements in large cohort studies.In this paper,we study the high-dimensional accelerated failure time(AFT)model under the case–cohort d...The case–cohort design has been widely used to reduce the cost of covariate measurements in large cohort studies.In this paper,we study the high-dimensional accelerated failure time(AFT)model under the case–cohort design.Based on?0-regularization and a newly defined weight function,we propose a weighted least squares procedure for variable selection and parameter estimation.Computationally,we develop a support detection and root finding(SDAR)algorithm,where the support is first determined based on the primal and dual information,then the estimator is obtained by solving the weighted least squares problem restricted to the estimated support.We show the proposed algorithm is essentially one Newton-type algorithm,thus it is more efficient and stable compared with other regularized methods.Theoretically,we establish a sharp error bound for the solution sequences generated from the proposed method.Furthermore,we propose an adaptive version of the proposed SDAR algorithm,which determines the support size of the estimated coefficient in a data-driven manner.Extensive simulation studies demonstrate the superior performance of the proposed procedures,especially for the computational efficiency.As an illustration,we apply the proposed method to a malignant breast tumor gene expression data.展开更多
基金supported by the NSFC (U1536206,61232016,U1405254,61373133, 61502242)BK20150925the PAPD fund
文摘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.
基金Supported by the National Natural Science Foundation of China(Grant Nos.12371274,12271459)National Social Science Foundation of China(Grant No.24CTJ036)+1 种基金Natural Science Foundation of Hubei Province(Grant No.2021CFB502)the Fundamental Research Funds for the Central Universities,Zhongnan University of Economics and Law(Grant No.2722024BY024)。
文摘The case–cohort design has been widely used to reduce the cost of covariate measurements in large cohort studies.In this paper,we study the high-dimensional accelerated failure time(AFT)model under the case–cohort design.Based on?0-regularization and a newly defined weight function,we propose a weighted least squares procedure for variable selection and parameter estimation.Computationally,we develop a support detection and root finding(SDAR)algorithm,where the support is first determined based on the primal and dual information,then the estimator is obtained by solving the weighted least squares problem restricted to the estimated support.We show the proposed algorithm is essentially one Newton-type algorithm,thus it is more efficient and stable compared with other regularized methods.Theoretically,we establish a sharp error bound for the solution sequences generated from the proposed method.Furthermore,we propose an adaptive version of the proposed SDAR algorithm,which determines the support size of the estimated coefficient in a data-driven manner.Extensive simulation studies demonstrate the superior performance of the proposed procedures,especially for the computational efficiency.As an illustration,we apply the proposed method to a malignant breast tumor gene expression data.
