A novel supervised manifold learning method was proposed to realize high accuracy face recognition under varying illuminant conditions. The proposed method, named illuminant locality preserving projections （ILPP）, e...A novel supervised manifold learning method was proposed to realize high accuracy face recognition under varying illuminant conditions. The proposed method, named illuminant locality preserving projections （ILPP）, exploited illuminant directions to alleviate the effect of illumination variations on face recognition. The face images were first projected into low dimensional subspace, Then the ILPP translated the face images along specific direction to reduce lighting variations in the face. The ILPP reduced the distance between face images of the same class, while increase the dis tance between face images of different classes. This proposed method was derived from the locality preserving projections （LPP） methods, and was designed to handle face images with various illumi nations. It preserved the face image＇ s local structure in low dimensional subspace. The ILPP meth od was compared with LPP and discriminant locality preserving projections （DLPP）, based on the YaleB face database. Experimental results showed the effectiveness of the proposed algorithm on the face recognition with various illuminations.展开更多
Sparsity preserving projection（SPP） is a popular graph-based dimensionality reduction（DR） method, which has been successfully applied to solve face recognition recently. SPP contains natural discriminating informa...Sparsity preserving projection（SPP） is a popular graph-based dimensionality reduction（DR） method, which has been successfully applied to solve face recognition recently. SPP contains natural discriminating information by preserving sparse reconstruction relationship of data sets. However, SPP suffers from the fact that every new feature learned from data sets is linear combinations of all the original features, which often makes it difficult to interpret the results. To address this issue, a novel DR method called dual-sparsity preserving projection （DSPP） is proposed to further impose sparsity constraints on the projection directions of SPP. Specifically, the proposed method casts the projection function learning of SPP into a regression-type optimization problem, and then the sparse projections can be efficiently computed by the related lasso algorithm. Experimental results from face databases demonstrate the effectiveness of the proposed algorithm.展开更多
This paper presents a set of algorithms capable of locating main facial features automatically and effectively. Based on integral projection of local binary image pixels and pixel clustering techniques, a set of a p...This paper presents a set of algorithms capable of locating main facial features automatically and effectively. Based on integral projection of local binary image pixels and pixel clustering techniques, a set of a priori knowledge based algorithms have succeeded in locating eyes, nose and mouth, and uprighting the tilt face. The proposed approach is superior to other methods as it takes account of photos with glasses and sha dows, therefore suitable for processing real ID type photos.展开更多
OBJECTIVE: To develop a more precise and accurate method, and identified a procedure to measure whether an acupoint had been correctly located.METHODS: On the face, we used an acupoint location from different acupunct...OBJECTIVE: To develop a more precise and accurate method, and identified a procedure to measure whether an acupoint had been correctly located.METHODS: On the face, we used an acupoint location from different acupuncture experts and obtained the most precise and accurate values of acupoint location based on the consistency information fusion algorithm, through a virtual simulation of the facial orientation coordinate system.RESULTS: Because of inconsistencies in each acupuncture expert's original data, the system error could not be modified using the characteristics ofthe general weight calculation. First, we corrected each expert of acupoint location system error itself,to obtain a rational quantification for each expert of acupuncture and moxibustion acupoint location consistent support degree, to obtain pointwise variable precision fusion results, to put every expert's acupuncture acupoint location fusion error enhanced to pointwise variable precision. Then, we more effectively used the measured characteristics of different acupuncture expert's acupoint location, to improve the measurement information utilization efficiency and acupuncture acupoint location precision and accuracy.CONCLUSION: Based on using the consistency matrix pointwise fusion method on the acupuncture experts' acupoint location values, each expert's acupoint location information could be calculated, and the most precise and accurate values of each expert's acupoint location could be obtained.展开更多
提出利用一种串、并行结合的方式将全局和局部面部特征进行集成:首先利用全局特征进行粗略的匹配,然后再将全局和局部特征集成起来进行精细的确认.在该方法中,全局和局部特征分别采用傅里叶变换和Gabor小波变换进行提取.两个大规模的人...提出利用一种串、并行结合的方式将全局和局部面部特征进行集成:首先利用全局特征进行粗略的匹配,然后再将全局和局部特征集成起来进行精细的确认.在该方法中,全局和局部特征分别采用傅里叶变换和Gabor小波变换进行提取.两个大规模的人脸库(FERET and FRGCv2.0)上的实验结果表明,此方法不仅可以显著提高系统的精度,而且可以提升系统的速度.展开更多
基金Supported by the National Natural Science Foundation of China(60772066)
文摘A novel supervised manifold learning method was proposed to realize high accuracy face recognition under varying illuminant conditions. The proposed method, named illuminant locality preserving projections （ILPP）, exploited illuminant directions to alleviate the effect of illumination variations on face recognition. The face images were first projected into low dimensional subspace, Then the ILPP translated the face images along specific direction to reduce lighting variations in the face. The ILPP reduced the distance between face images of the same class, while increase the dis tance between face images of different classes. This proposed method was derived from the locality preserving projections （LPP） methods, and was designed to handle face images with various illumi nations. It preserved the face image＇ s local structure in low dimensional subspace. The ILPP meth od was compared with LPP and discriminant locality preserving projections （DLPP）, based on the YaleB face database. Experimental results showed the effectiveness of the proposed algorithm on the face recognition with various illuminations.
基金Supported by the National Natural Science Foundation of China(11076015)the Shandong Provincial Natural Science Foundation(ZR2010FL011)the Scientific Foundation of Liaocheng University(X10010)~~
文摘Sparsity preserving projection（SPP） is a popular graph-based dimensionality reduction（DR） method, which has been successfully applied to solve face recognition recently. SPP contains natural discriminating information by preserving sparse reconstruction relationship of data sets. However, SPP suffers from the fact that every new feature learned from data sets is linear combinations of all the original features, which often makes it difficult to interpret the results. To address this issue, a novel DR method called dual-sparsity preserving projection （DSPP） is proposed to further impose sparsity constraints on the projection directions of SPP. Specifically, the proposed method casts the projection function learning of SPP into a regression-type optimization problem, and then the sparse projections can be efficiently computed by the related lasso algorithm. Experimental results from face databases demonstrate the effectiveness of the proposed algorithm.
文摘This paper presents a set of algorithms capable of locating main facial features automatically and effectively. Based on integral projection of local binary image pixels and pixel clustering techniques, a set of a priori knowledge based algorithms have succeeded in locating eyes, nose and mouth, and uprighting the tilt face. The proposed approach is superior to other methods as it takes account of photos with glasses and sha dows, therefore suitable for processing real ID type photos.
基金Supported by the Key Program of State Administration of Traditional Chinese Medicine of China:the Science of Acupuncture and Moxibustion(No.ZYSNXD-CC-ZDXK-07)
文摘OBJECTIVE: To develop a more precise and accurate method, and identified a procedure to measure whether an acupoint had been correctly located.METHODS: On the face, we used an acupoint location from different acupuncture experts and obtained the most precise and accurate values of acupoint location based on the consistency information fusion algorithm, through a virtual simulation of the facial orientation coordinate system.RESULTS: Because of inconsistencies in each acupuncture expert's original data, the system error could not be modified using the characteristics ofthe general weight calculation. First, we corrected each expert of acupoint location system error itself,to obtain a rational quantification for each expert of acupuncture and moxibustion acupoint location consistent support degree, to obtain pointwise variable precision fusion results, to put every expert's acupuncture acupoint location fusion error enhanced to pointwise variable precision. Then, we more effectively used the measured characteristics of different acupuncture expert's acupoint location, to improve the measurement information utilization efficiency and acupuncture acupoint location precision and accuracy.CONCLUSION: Based on using the consistency matrix pointwise fusion method on the acupuncture experts' acupoint location values, each expert's acupoint location information could be calculated, and the most precise and accurate values of each expert's acupoint location could be obtained.
文摘提出利用一种串、并行结合的方式将全局和局部面部特征进行集成:首先利用全局特征进行粗略的匹配,然后再将全局和局部特征集成起来进行精细的确认.在该方法中,全局和局部特征分别采用傅里叶变换和Gabor小波变换进行提取.两个大规模的人脸库(FERET and FRGCv2.0)上的实验结果表明,此方法不仅可以显著提高系统的精度,而且可以提升系统的速度.
