[1]杨章静,刘传才,顾兴健,等.依概率分类的保持投影及其在人脸识别中的应用[J].南京理工大学学报(自然科学版),2013,37(01):7.
 Yang Zhangjing,Liu Chuancai,Gu Xingjian,et al.Probabilistic classification preserving projections and its application to face recognition[J].Journal of Nanjing University of Science and Technology,2013,37(01):7.
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依概率分类的保持投影及其在人脸识别中的应用
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
37卷
期数:
2013年01期
页码:
7
栏目:
出版日期:
2013-02-28

文章信息/Info

Title:
Probabilistic classification preserving projections and its application to face recognition
作者:
杨章静刘传才顾兴健朱俊
南京理工大学 计算机科学与工程学院,江苏 南京 210094
Author(s):
Yang ZhangjingLiu ChuancaiGu XingjianZhu Jun
School of Computer Science and Engineering,NUST,Nanjing 210094,China
关键词:
人脸识别特征提取降维流形局部保持投影
Keywords:
face recognitionfeature extractiondimensionality reductionmanifoldlocality preserving projections
分类号:
TP391.4
摘要:
为了提高低维空间对原始高维样本的表示能力,该文提出了依概率分类的保持投影算法(PCPP)。PCPP考虑了样本类别信息,并重新定义类内样本间的相似性,包含样本的邻域信息,而且在K近邻选择下,还能反映样本被正确归类的概率。样本经投影后,在低维特征空间内,被正确归类且概率较大的类内样本间的邻域关系得到了保持。在Yale、FERET及AR人脸库上的人脸识别实验表明,PCPP较其他算法取得了更好的识别性能。
Abstract:
In order to improve the ability of low dimensional space to represent highdimensional samples,a novel manifold learning method called probabilistic classification maintain projection(PCPP) is proposed.The PCPP takes class information into account and refines similarity weights of intraclass samples,which not only contain neighborhood information of samples,but also can reflect the probability that a sample can be correctly classified when its K nearest neighbors are selected.After projection,neighborhood relationship of the intraclass samples which possess more classification probability can be preserved.Experimental results on the Yale,FERET and AR face databases demonstrate that the PCPP performs better than other algorithms.

参考文献/References:

[1]Turk M,Pentland A.Eigenfaces for recognition [J].Journal of Cognitive Neuroscience,1991,3(1):71-86.
[2]Belhumeur P N,Hespanha J P,Kriegman D J.Eigenfaces vs fisherfaces:Recognition using class specific linear projection [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):711-720.
[3]Roweis S T,Saul L K.Nonlinear dimensionality reduction by locally linear embedding [J].Science,2000,290:2323-2326.
[4]Tenenbanm J B,Silva V De,Langford J C.A global fecmetric framework for nonlinear dimensionality reduction [J].Science,2000,290:2319-2322.
[5]Belkin M,Niyogi P.Laplacian eigenmap for dimensionality reduction and data representation [J].Neural Computation,2003,15(6):1373-1396.
[6]He X,Yang S,Hu Y,et al.Face recognition using laplacianfaces [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(3):328-340.
[7]Zhao H,Sun S,Jing Z,et al.Local structure based supervised feature extraction [J].Pattern Recognition,2006,39:1546-1550.
[8]Yan S C,Xu D,Zhang B Y,et al.Graph embedding and extensions:A general framework for dimensionality reduction [J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(1):40-51.
[9]Sugiyama M.Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis[J].Journal of Machine Learning Research,2007,8(5):1027-1061.
[10]Li B,Wang C,Huang D S.Supervised feature extraction based on orthogonal discriminant projection[J].Neurocomputing,2009,73(1-3):191-196.
[11]Zhang S W,Li Y K,Wu Y H,et al.Modified orthogonal discriminant projection for classification[J].Neurocomputing,2011,74(17):3690-3694.
[12]Yang J,Zhang D,Yang J Y,et al.Globally maximizing,locally minimizing:Unsupervised discriminant projection with applications to face and palm biometrics[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(4):650-664.
[13]殷俊,金忠.完备非监督鉴别投影与人脸图像分析[J].计算机辅助设计与图形学学报,2010,22(11):1912-1917. Yin Jun,Jin Zhong.Complete unsupervised discriminant projection and face image analysis [J].Journal of ComputerAided Design & Computer Graphics,2010,22(11):1912-1917.
[14]严慧,杨靖宇.无监督的差分鉴别特征提取以及在人脸识别上的应用[J].计算机辅助设计与图形学学报,2009,21(11):1632-1637. Yan Hui,Yang Jingyu.Unsupervised difference discriminant feature extractionwith application to face recognition [J].Journal of ComputerAided Design & Computer Graphics,2009,21(11):1632-1637.
[15]Wu S,Sun M,Yang J.Stochastic neighbor projection on manifold for feature extraction [J].Neurocomputing,2011,74(17):2780-2789.
[16]Jacob G,Sam R,Geoff H,et al.Neighbourhood components analysis[A].Proceedings of NIPS 2004,Advances in Neural Information Processing Systems[C].Vancouver,Canada:MIT Press,2004:513-520.
[17]He Xiaofei,Yan Shuicheng,Hu Yuxiao,et al.Learning a locality preserving subspace for visual recognition[A].Proceedings of Ninth IEEE International Conference on Computer Vision[C].Nice,France.IEEE Computer Society,2003:385-392.

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备注/Memo

备注/Memo:
基金项目:国家自然科学基金(60632050);国防科工局高分专项(民用部分)(E0310/1112/JC01)
作者简介:杨章静(1979-),男,博士生,主要研究方向:图像处理、人脸识别,Email:yzzjjj@126.com;
通讯作者:刘传才(1963-),男,博士,教授,博士生导师,主要研究方向:计算机视觉、图像处理与分析、智能机器人的视觉与导航系统,Email:chcailiu@163.com。
更新日期/Last Update: 2013-02-15