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
Multi-way principal component analysis (MPCA) is the most widely utilized multivariate statistical process control method for batch processes. Previous research on MPCA has commonly agreed that it is not a suitable me...Multi-way principal component analysis (MPCA) is the most widely utilized multivariate statistical process control method for batch processes. Previous research on MPCA has commonly agreed that it is not a suitable method for multiphase batch process analysis. In this paper, abundant phase information is revealed by way of partitioning MPCA model, and a new phase identification method based on global dynamic information is proposed. The application to injection molding shows that it is a feasible and effective method for multiphase batch process knowledge understanding, phase division and process monitoring.展开更多
Abstract： In the present study, we established an ultra performance liquid chromatography coupled with time-of-flight mass spectrometry （UPLC-QTOF-MSE） method to simultaneously quantify 33 components in Ginkgo bilo...Abstract： In the present study, we established an ultra performance liquid chromatography coupled with time-of-flight mass spectrometry （UPLC-QTOF-MSE） method to simultaneously quantify 33 components in Ginkgo biloba leaf extracts （GBEs）, including 17 flavonol glycosides, five terpene trilactones （TTLs）, four polyphenols and seven carboxylic acids. This optimized method was successfully applied to analyze the explicit compositions of GBE samples collected from different places. Furthermore, the data were processed through unsupervised principal component analysis （PCA） and supervised orthogonal partial least squared discrimination analysis （OPLS-DA） to evaluate the quality and compare the differences between the samples according to the contents of the 33 chemical constituents. Bilobalide, protocatechuic acid, shikimic acid, quinic acid, ginkgolide B, ginkgolide J, kaempferol-3-O-rutinoside, isorhamnetin-3-O-rutinoside, quercetin-3-O-ct-L-rhamnopyranocyl-2＂-（6＇＂-p-coumaroyl）-β-D-glucoside and rutin were recognized as characteristic chemical markers that contributed most to control the quality of GBEs. Based on the fact that GBEs should be standardized with the characteristic components as quality control chemical markers, it is most important to maintain the quality of GBEs stable and reliable, and this method also provided a good strategy to further rectify and standardize the GBEs market.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 Guangzhou Scientific and Technological Project (2012J5100032)Nansha District Independent Innovation Project (201103003)
文摘Multi-way principal component analysis (MPCA) is the most widely utilized multivariate statistical process control method for batch processes. Previous research on MPCA has commonly agreed that it is not a suitable method for multiphase batch process analysis. In this paper, abundant phase information is revealed by way of partitioning MPCA model, and a new phase identification method based on global dynamic information is proposed. The application to injection molding shows that it is a feasible and effective method for multiphase batch process knowledge understanding, phase division and process monitoring.
文摘Abstract： In the present study, we established an ultra performance liquid chromatography coupled with time-of-flight mass spectrometry （UPLC-QTOF-MSE） method to simultaneously quantify 33 components in Ginkgo biloba leaf extracts （GBEs）, including 17 flavonol glycosides, five terpene trilactones （TTLs）, four polyphenols and seven carboxylic acids. This optimized method was successfully applied to analyze the explicit compositions of GBE samples collected from different places. Furthermore, the data were processed through unsupervised principal component analysis （PCA） and supervised orthogonal partial least squared discrimination analysis （OPLS-DA） to evaluate the quality and compare the differences between the samples according to the contents of the 33 chemical constituents. Bilobalide, protocatechuic acid, shikimic acid, quinic acid, ginkgolide B, ginkgolide J, kaempferol-3-O-rutinoside, isorhamnetin-3-O-rutinoside, quercetin-3-O-ct-L-rhamnopyranocyl-2＂-（6＇＂-p-coumaroyl）-β-D-glucoside and rutin were recognized as characteristic chemical markers that contributed most to control the quality of GBEs. Based on the fact that GBEs should be standardized with the characteristic components as quality control chemical markers, it is most important to maintain the quality of GBEs stable and reliable, and this method also provided a good strategy to further rectify and standardize the GBEs market.
