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
In this paper, we propose an improved Directed Acyclic Graph Support Vector Machine （DAGSVM） for multi-class classification. Compared with the traditional DAGSVM, the improved version has advantages that the structu...In this paper, we propose an improved Directed Acyclic Graph Support Vector Machine （DAGSVM） for multi-class classification. Compared with the traditional DAGSVM, the improved version has advantages that the structure of the directed acyclic graph is not chosen random and fixed, and it can be adaptive to be optimal according to the incoming testing samples, thus it has a good generalization performance. From experiments on six datasets, we can see that the proposed improved version of DAGSVM is better than the traditional one with respect to the accuracy rate.展开更多
Currently there are two approaches for a multi-class support vector classifier(SVC). One is to construct and combine several binary classifiers while the other is to directly consider all classes of data in one optimi...Currently there are two approaches for a multi-class support vector classifier(SVC). One is to construct and combine several binary classifiers while the other is to directly consider all classes of data in one optimization formulation. For a K-class problem(K>2),the first approach has to construct at least K classifiers,and the second approach has to solve a much larger op-timization problem proportional to K by the algorithms developed so far. In this paper,following the second approach,we present a novel multi-class large margin classifier(MLMC). This new machine can solve K-class problems in one optimization formula-tion without increasing the size of the quadratic programming(QP) problem proportional to K. This property allows us to construct just one classifier with as few variables in the QP problem as possible to classify multi-class data,and we can gain the advantage of speed from it especially when K is large. Our experiments indicate that MLMC almost works as well as(sometimes better than) many other multi-class SVCs for some benchmark data classification problems,and obtains a reasonable performance in face recognition application on the AR face database.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.
文摘In this paper, we propose an improved Directed Acyclic Graph Support Vector Machine （DAGSVM） for multi-class classification. Compared with the traditional DAGSVM, the improved version has advantages that the structure of the directed acyclic graph is not chosen random and fixed, and it can be adaptive to be optimal according to the incoming testing samples, thus it has a good generalization performance. From experiments on six datasets, we can see that the proposed improved version of DAGSVM is better than the traditional one with respect to the accuracy rate.
基金supported by the National Natural Science Foundation of China (No. 60675049)the National Creative Research Groups Science Foundation of China (No. 60721062)the Natural Science Foundation of Zhejiang Province, China (No. Y106414)
文摘Currently there are two approaches for a multi-class support vector classifier(SVC). One is to construct and combine several binary classifiers while the other is to directly consider all classes of data in one optimization formulation. For a K-class problem(K>2),the first approach has to construct at least K classifiers,and the second approach has to solve a much larger op-timization problem proportional to K by the algorithms developed so far. In this paper,following the second approach,we present a novel multi-class large margin classifier(MLMC). This new machine can solve K-class problems in one optimization formula-tion without increasing the size of the quadratic programming(QP) problem proportional to K. This property allows us to construct just one classifier with as few variables in the QP problem as possible to classify multi-class data,and we can gain the advantage of speed from it especially when K is large. Our experiments indicate that MLMC almost works as well as(sometimes better than) many other multi-class SVCs for some benchmark data classification problems,and obtains a reasonable performance in face recognition application on the AR face database.
