人脸识别中高维数据特征分析
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摘要
人脸识别近年来在模式识别领域受到高度关注。作为一种主要的生物识别技术,在金融安全、电子商务与数字娱乐等领域具有广泛的应用前景。经过近半个世纪的发展,在人脸识别研究领域已经取得许多成果,基本实现了特定环境下的准确识别。尽管如此,人脸识别技术要达到完全实用水平,还面临着诸多挑战,因人脸数据维数过高带来的大规模数据存储和计算问题就是其中之一。
本文针对人脸识别中的高维数据降维及识别问题展开深入研究,主要研究工作与创新点如下:
1)研究了基于典型线性与非线性降维算法的人脸图像高维数据的降维问题。深入分析了不同算法的降维性能;基于剩余残差的维数评价模型,对人脸图像的本征维数进行了讨论;比较了Isomap算法的不同邻域参数k对降维结果的影响,并在ORL、Yale、Feret人脸库上进行对比试验。试验结果表明,非线性降维算法Isomap的降维效果优于典型的线性降维方法PCA。
2)研究了基于非线性降维的人脸识别方法。为了解决新样本在训练空间的映射及降维问题,引入了增量式Isomap算法,提出了利用距离保持的增量式(IADP-Isomap)的人脸识别方法,首先对人脸图像应用Isomap,然后采用IADP-Isomap获得新样本的低维特征表示,采用最近邻分类器进行分类。实验结果展示了该方法的可行性。
3)探讨了非负矩阵分解(NMF)算法的原理及在人脸识别中的应用。基于NMF的人脸识别方法在人脸图像的光照、姿态与表情改变时性能会大幅下降,针对该问题,本文提出NMF+SDA方法对之改进,首先对人脸图像应用NMF,然后融合线性鉴别分析(LDA)和奇异值分解(SVD)的思想,进行人脸高维数据降维和特征提取。在人脸库上的实验表明,本方法具有较高的识别率。
4)基于非线性降维算法,引入模糊数学中矢量隶属函数和隶属度,提出了一种基于模糊隶属函数的三控制要素的多项式模糊拟合算法。利用归一化贴近度可以评价拟合曲线的线性度。将此算法用于人脸识别,实验结果表明具有较好的识别率。
In recent years, the face recognition has attracted considerable attention within the community of pattern recognition. As one of the most successful branches of biometrics, it has great potential applications in finance security, electronic commerce, and digital entertainment, etc. Over the past half of century, the face recognition has developed rapidly. Now under the controlled conditions, face recognition systems have achieved good results. However, a great number of challenges are still leaved to resolve before one can implement a robust and practical face recognition application. Among these challenges, the large-scale data storage and computation arising from excessively high face data is one of the most difficult.
Our work is focusing on the dimension reduction of face data and recognition problem. The work and the innovation in this dissertation can be summarized as following.
(1) The dimensionality reduction problem of face data based on typical linear and nonlinear dimensionality reduction algorithms is investigated. Meanwhile, the performance of these algorithms is intensively analyzed. Based on the residual variance evaluation model, this dissertation discusses the intrinsic dimension of facial images. Following that, the influences of neighborhood parameters k on dimensionality reduction are taken into account. Experiments on the ORL, Yale, and Feret face database show the performance of nonlinear dimensionality reduction algorithms is better than that of linear ones.
(2) The face recognition method based on the nonlinear dimensionality reduction algorithm is studied. Firstly, this dissertation introduces the incremental Isomap algorithm to resolve the novel samples’mapping and dimensionality reduction problem in the training space. Following that, a face recognition method based on IADP-Isomap is proposed. The experimental results show that the recognition method is feasible.
(3) The non-negative matrix factorization (NMF) algorithm and its application in face recognition is discussed. On condition of the variation of illumination, poses, and expression, the performance of NMF-based recognition method would dramatic decreases. Focusing on this problem, this dissertation proposes a so-called NMF+SDA algorithm. It can effectively implement dimensionality reduction and feature extraction of the face dada. Experiments on face database exhibit that NMF+SDA owns better recognition rates than traditional NMF.
(4) Based on the nonlinear dimensionality reduction algorithm, the concepts of vector membership function and membership degree in fuzzy mathematics are introduced. It is presented that the fuzzy matching for a nonlinear function between input and output can be realized by using three rulers (two point rulers and one slope ruler). The affinity between two memberships can be used for assessment to the linearity of the matched curve. Consequently, the algorithm of polynomial fuzzy matching based on three rulers is proposed and applied in face recognition. Experimental results demonstrate the recognition algorithm is feasible and has good recognition capability.
本文针对人脸识别中的高维数据降维及识别问题展开深入研究,主要研究工作与创新点如下:
1)研究了基于典型线性与非线性降维算法的人脸图像高维数据的降维问题。深入分析了不同算法的降维性能;基于剩余残差的维数评价模型,对人脸图像的本征维数进行了讨论;比较了Isomap算法的不同邻域参数k对降维结果的影响,并在ORL、Yale、Feret人脸库上进行对比试验。试验结果表明,非线性降维算法Isomap的降维效果优于典型的线性降维方法PCA。
2)研究了基于非线性降维的人脸识别方法。为了解决新样本在训练空间的映射及降维问题,引入了增量式Isomap算法,提出了利用距离保持的增量式(IADP-Isomap)的人脸识别方法,首先对人脸图像应用Isomap,然后采用IADP-Isomap获得新样本的低维特征表示,采用最近邻分类器进行分类。实验结果展示了该方法的可行性。
3)探讨了非负矩阵分解(NMF)算法的原理及在人脸识别中的应用。基于NMF的人脸识别方法在人脸图像的光照、姿态与表情改变时性能会大幅下降,针对该问题,本文提出NMF+SDA方法对之改进,首先对人脸图像应用NMF,然后融合线性鉴别分析(LDA)和奇异值分解(SVD)的思想,进行人脸高维数据降维和特征提取。在人脸库上的实验表明,本方法具有较高的识别率。
4)基于非线性降维算法,引入模糊数学中矢量隶属函数和隶属度,提出了一种基于模糊隶属函数的三控制要素的多项式模糊拟合算法。利用归一化贴近度可以评价拟合曲线的线性度。将此算法用于人脸识别,实验结果表明具有较好的识别率。
In recent years, the face recognition has attracted considerable attention within the community of pattern recognition. As one of the most successful branches of biometrics, it has great potential applications in finance security, electronic commerce, and digital entertainment, etc. Over the past half of century, the face recognition has developed rapidly. Now under the controlled conditions, face recognition systems have achieved good results. However, a great number of challenges are still leaved to resolve before one can implement a robust and practical face recognition application. Among these challenges, the large-scale data storage and computation arising from excessively high face data is one of the most difficult.
Our work is focusing on the dimension reduction of face data and recognition problem. The work and the innovation in this dissertation can be summarized as following.
(1) The dimensionality reduction problem of face data based on typical linear and nonlinear dimensionality reduction algorithms is investigated. Meanwhile, the performance of these algorithms is intensively analyzed. Based on the residual variance evaluation model, this dissertation discusses the intrinsic dimension of facial images. Following that, the influences of neighborhood parameters k on dimensionality reduction are taken into account. Experiments on the ORL, Yale, and Feret face database show the performance of nonlinear dimensionality reduction algorithms is better than that of linear ones.
(2) The face recognition method based on the nonlinear dimensionality reduction algorithm is studied. Firstly, this dissertation introduces the incremental Isomap algorithm to resolve the novel samples’mapping and dimensionality reduction problem in the training space. Following that, a face recognition method based on IADP-Isomap is proposed. The experimental results show that the recognition method is feasible.
(3) The non-negative matrix factorization (NMF) algorithm and its application in face recognition is discussed. On condition of the variation of illumination, poses, and expression, the performance of NMF-based recognition method would dramatic decreases. Focusing on this problem, this dissertation proposes a so-called NMF+SDA algorithm. It can effectively implement dimensionality reduction and feature extraction of the face dada. Experiments on face database exhibit that NMF+SDA owns better recognition rates than traditional NMF.
(4) Based on the nonlinear dimensionality reduction algorithm, the concepts of vector membership function and membership degree in fuzzy mathematics are introduced. It is presented that the fuzzy matching for a nonlinear function between input and output can be realized by using three rulers (two point rulers and one slope ruler). The affinity between two memberships can be used for assessment to the linearity of the matched curve. Consequently, the algorithm of polynomial fuzzy matching based on three rulers is proposed and applied in face recognition. Experimental results demonstrate the recognition algorithm is feasible and has good recognition capability.
引文
[1] E. Montseny, J. Frau. Computer vision :specialized processors for real-time image analysis,Workshop proceedings, Barcelona, Spain, September 1991.)
[2]张翠平,苏光大.人脸识别技术综述.中国图像图形学报,第5卷(A版)第11期2000,885-894
[3] R.Chellappa, C.L. Wilson, and S. Sirohey. Human and machine recognition of faces: A survey.In: Proc. IEEE, 83:705-741, May 1995
[4] W. Zhao, R. Chellappa, A. Rosenfeld, P.J. Phillips. Face Recognition: A Literature Survey. url="citeseer.nj.nec.com/374297.html"
[5] E. Montseny, J. Frau. Computer vision :specialized processors for real-time image analysis,Workshop proceedings, Barcelona, Spain, September 1991.
[6] M.Bichsel and A. Pentlend, Human face recognition and the face image set’s topology, CVGIP:Image Understanding 59(2), 1994, 254-261.
[7] Roberto Brunelli and Tomaso Poggio Face Recognition: Features versus Template, IEEE Transaction Pattern Analysis and Machine Intelligence Vol. 15 .no.10 pp1042-1052, 1993
[8] Turk M, Pentland A. Eigen-faces for Recognition Journal of cognitive neuroscience, 3(1),pp71-86, 1991
[9] Babak Moghaddam, Chahab Nastar and Alex Pentland, A Bayesian Similarity Measure for Direct Image Matching, International Conference on Pattern Recognition, Vienna, Austria,August 1996
[10] Baback Moghaddam, Wasiuddin Wahid and Alex Pentland. Beyond Eigenfaces: Probabilistic Matching for Face Recognition, the 3rd IEE Int. Con. On Auto. Face- and Gesture- Recognition,Nara, Japan, 1998.4
[11] Zi-Quan Hong. Algebraic feature extraction of image for recognition. Pattern Recognition,24(3), pp211-219, 1991
[12] M.Bichsel and A. Pentlend, Human face recognition and the face image set’s topology, CVGIP:Image Understanding 59(2), 1994, 254-261.
[13] Laurenz Wiskott, jean-Marc Fellous, Norbert Kruger, and Christoph von der Malsburg. Face Recognition by Elastic Graph Matching. In: IEEE Transations on Pattern Analysis and Machine Intelligence, 19(7), 1997, 775—779
[14] A.V.Nefian and M.H.Hayes. Face detection and recognition using Hidden Markov Models in International Conference on Image Processing, 1998
[15] Ming-Hsuan Yang, David Kriegman, and Narendra Ahuja,.Detecting Faces in Images: A Survey, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 24, no.1, pp. 34-58, 2002
[16] F.Sarnaria. Face recognition using Hidden Markov Models. Ph.D thesis, University of Cambridge, 1994
[17] Brunelli R,Poggio T. Face recognition:features versus templates.IEEE Trans. PAMI, 1993. 15(10): 1042-1052
[18] N. Roder, X. Li. Accuracy Analysis for Facial Feature Detection. Pattern Recognition, 1996, 29(1): 143~157
[19] K. M. Lam, H. Yan. Locating and Extracting the Eye in Human Face Images. Pattern Recognition, 1996, 29(5): 143~157
[20] J. Y. Deng, F. Lai. Region-based Template Deformation and Masking for Eye-feature Extraction and Description. Pattern recognition, 1997, 30(3): 403~419
[21] Yuille A L.Deformable templates for face recognition[J].Journal of Cognitive Neuroscience, 1991,3(1): 71-86.
[22] Brunelli R,Poggio T.Face recognition:features versus templates[J].IEEE Transactions on PAMI, 1993, 15(10):1042-1052.
[23] H. Peng, D. Zhang, Dual Eigenspace Method for Human Face Recognition. Electronics Letters, 1997, 33(4): 283~274
[24] D. L. Swets,J. J. Weng, Using Discriminant Eigenfeatures for image retrieval. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1996, 18(8): 831~836
[25] P. N. Belhumeur, J. P. Hespanha, D. J. Kriegman, Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1996, 18(7): 711~720
[26] BARTLETT M S,MOVELLAN J R,SEJNOWSKI T J.Face recognition by independent component analysis.IEEE Transactions on Neural Networks,2002,13(6):1450一1464
[27] BAEK K,DRAPER B A,BEVERIDGE J R.PCA vs.ICA:A comparison on the feret data set. Intelnational Conference on Computer Vision,Pattern Recognition and Image,2002,824一827
[28] Lee D D, Seung H S. Leaming the parts of objects by non-negative matrix factorization [J]. Nature (S0028-0836), 1999, 401(21): 788-791
[29] Turk M, Pentland A. Eigenfaces for Recognition [J]. Journal of Cognitive Neuroscience (S0898-929X), 1991, 3(1): 71-86
[30] Turk M, Pentland A. Face Recognition Using Eigenfaces [C] Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Maui: IEEE, 1991: 586-591
[31] Li S Z, Hou X W, Zhang H J. Learning spatially localized, parts-based representation [C]// Int. Conf. Computer Vision and Pattern Recognition, Washington, USA: IEEE Computer Society, 2001: 207-212
[32] Buciu Ioan, Pitas Ioannis. Application of non negative and local non negative matrix factorization to facial expression recognition [C]. Proceedings of the 17th International Conference on Pattern Recognition (ICPR’04), Washington, USA: IEEE Computer Society, 2004: 288-291
[33] Chen X, Gu L, Li S Z, et al. Learning representative local features for face detection [C]. IEEE Proceedings of Computer Vision and Pattern Recognition, Kauai, USA: IEEE , 2001: 1126-1131
[34] Zafeiriou S, Tefas A, Pitas I. Discriminant NMFfaces for Frontal Face Verification [C]. Machine Learning for Signal Processing (MLSP 2005), Mystic. USA: IEEE, 2005: 355-359.
[35] L.E. Baum, T. Petrie. Statistical Inference for Probabilistic of Finite Markov Chains, Annals of Mathematical Statistics, 1966, 37(12): 1554~1563
[36] L.E. Baum, J. A. Egon. An Inequality with Applications to Statistical Estimation for Probabilistic Functions of a Markov Process and to a Model for Ecology. Bulletin of the American Medical Society, 1967, 73:360~363
[37] L.E. Baum, G. R. Sell, Growth Functions for Translations on Manifolds. Pac. J. Math., 1968, 27(2): 211~227
[38] L.E. Baum, T. Petrie, G. Soules, et al. A Maxinization Technique Occurring in the Statistical Analysis of Probabilistic Function of Markov Chains, Annals of Mathematical Statistics, 1970, 41(1): 164~171
[39] L.E. Baum. An Inequality and Associated Maximization Technique in Statistical Estimation for Probabilistic Functions of Markov Process. Inequalities, 1972, 3(1):1~8
[40] F. Smaria, S. Young, HMM Based Architecture for Face Recognition. Image and Computer Vision, 1994, 12(8): 537~543.
[41] F. Samaria. Face Recognition Using Hidden Markov Models. Ph.D. Fissertation , University of Cambridge, 1994.
[42] A. V. Nefian, M. H. Hayes, A Hidden Markov for face recognition. In proc. ICASSP, 1998, pages: 2721~2724.
[43] R. Brunelli, T. Poggio. Face Recognition: Features Versus Templates. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1993, 15(10): 1042~1052.
[44]李刚,高政.人脸识别理论研究进展.计算机与现代化,2003,3
[45] Lin S H, Kung S Y,Lin J.Face Recognition /Detection by Probabilistic Decision Based Neural Network.IEEE Trans on Neural Networks,1997,8(1):114-134
[46] Nastar C,Ayachc N. Frequency-based nonrigid motion analysis:application to four dimensional medical images. IEEE Trans. PAMI,1996,18(11):1067-1079
[47] M. Lades,J. Vorbuggen, J. Buhmann, et al. Distortion Invariant Object Recognition in the Dynamic Link Architecture. IEEE Trans. On Computers, 1991, 42(3): 300~311.
[48] C. Kotropoulos, I. Pitas. Face authentication based on morphological grid matching. IEEE inter. Conf. on Image Processing, 1997, pages:105~108.
[49] L. Wiskott, J. M. Fellous, N. kruger, et al. Face Recognition by Elastic Bunch Graph Matching. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1997, 19(7): 775~779.
[50] L. Wiskott. Phantom Faces for Face Analysis. Pattern Recognition, 1997, 30(6): 1209~1230
[51] O. Nakamura, S. Mathur, T. Minami. Identification of Human Faces Based on Isodensity Maps. Pattern recognition, 1991, 24(3): 263~272.
[52] P. Kruizinga和N. Petkov. Person Identification Based on Multiscale Matching of Cortical Images. In proc. HPCN Europe, 1995, Pages: 420-427.
[53] Donoho D L,Grimes C. Hessian eigenmaps: New locally linear embedding techniques for high-dimensional data. Proceedings of the National Academy of Sciences,2003,100 (10): 5591-5596
[54] Zhang C S,Wang J,Zhao N Y,et al. Reconstruction and analysis of multi-pose face images based on nonlinear dimensionality reduction. Pattern Recognition,2004,37(l):325-336
[55] Weinberger K Q , Saul L K. Unsupervised learning of image manifolds by semidefinite programming. International Journal of Computer Vision,2006,70(1):77-90
[56] Belkin M,Niyogi P. Semi-Supervised learning on Riemannian manifolds. Machine Learning,2004,56(1): 209-239
[57] Belkin M,Niyogi P,Sindhwani V. On manifold regularization. Proceedings of the International Conference on Aland Statistics,2005:17-24
[58] Roweis S,Saul L,Hinton G. Global coordination of local linear models. Advances in Neural Information Processing Systems,2002:889-896
[59] Brand M. Minimax embeddings. Advances in Neural Information Processing Systems,2004,16:505-512
[60]赵连伟,罗四维,赵艳敞.高维数据的低维嵌入及嵌入维数研究.软件学报,2005,16 (8):1423-1430
[61]何力,张军平,周志平.基于放大因子和延伸方向研究流形学习算法.计算机学报,2005 28(12):2000-2009
[62] Zhang Z,Zha H. Principal manifold sand nonlinear dimension reduction via local tangent space alignment. SIAM Journal of Scientific Computing,2004,26(1):313-338
[63] K. Fukunaga and D. R. Olsen. An algorithm for finding intrinsic dimensionality of data. IEEE Transactions on Computers, 20(2):176–193, 1971
[64] Lawrence K.Saul; Sam T.Roweis; Think Globally, Fit Locally: Unsupervised Learning ofNonlinear Manifolds; Technical Report MS CIS-01-18, Univ. of Pennsylvania 2002
[65] James M.Lattin,J.Douglas Carrol,Paul E.Green,多元数据分析(英文版),机械工业出版社,北京,2003年7月,91-101,211-218
[66]方驰,丁晓青,吴佑寿.基于PCA的脱机手写汉字的统计模型及其应用.模式识别与人工智能,2001,14(1)
[67]徐蓉,姜峰,姚鸿勋.流形学习概述.智能系统学报,2006,1(1):44-51
[68] C.M. Bishop, Neural Networks for Pattern Recognition. Oxford:Clarendon Press,1995
[69] N.Vlassis Y. Motomura and B. Krose. Supervised dimension reduction ofintrinsically low-dimensional data. Neural Computation, 14(1):191-215, January 2002
[70] Joshua B. Tenenbaum et al, A Global Geometric Framework for NonlinearDimensionality Reduction, Science, Vol 290, December 22, 2000.
[71] J. A. Lee, A. Lendasse and M. Verleysen. Curvilinear Distance Analysis versus Isomap ESANN 2002 proceedings - European Symposium on Artificial Neural Networks , Bruges (Belgium), 24-26 April 2002, 185-192.
[72] Sam T. Roweis et al, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, Vol 290, December 22, 2000
[73] L. K. Saul and S. T. Roweis. Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds. Journal of Machine Learning Research, v4, pp.119-155, 2003.
[74] Zhenyue Zhang and Hongyuan Zha. Local Linear Smoothing for NonlinearManifold Learning. CSE-03-003, Technical Report, CSE, Penn State Univ., 2003.
[75] de Ritter D, Kouropteva O, Okun O, Pietik?inen M & Duin RPW. Supervised locally linear embedding. Artificial Neural Networks and Neural Information Processing, ICANN/ICONIP 2003 Proceedings, Lecture Notes in Computer Science 2714, Springer, 333-341
[76] V. de Silva, J. B. Tenenbaum, Global versus local methods in nonlinear dimensionality reduction. (2002), Advances in Neural Information Processing Systems 15. M.SBecker, S., Thrun, S., and Obemayer, K. (eds). Cambridge, MITPress, 2002, 705-712
[77]侯越先.基于自组织的鲁棒非线性维数约减算法.计算机研究与发展,2005.2
[78] J. B. Kruskal.Multidimensional scaling by optimizing goodness of fit to a non-metric hypothesis. Psychometrika, 1964,29(1):1-27
[79] J. B. Tenenbaum, V. de Silva, J. C. Langford . A global geometric framework for nonlinear dimensionality reduction. Science 290 (5500): 2319-2323, 22 December 2000
[80] M.H.C. Law,N. Zhang,A.K. Jain. Nonlinear manifold learning for data stream.Proceedings of SIAM Data Mining,Florida,USA,2004,33~44
[81] Lukui Shi,Pilian He,Enhai Liu. An incremental nonlinear dimensionality reduction algorithm based on ISOMAP. Proceedings of AI05,LNAI 3809,Sydney,2005,892~895
[82] Daniel D. Lee,H, Sebastian Seung. Learning the partsof objects by non-negative matrix factorization, Nature ,401,788-791,1999
[83] Lee D D, Seung H S. Algorithms for non-negative matrix factorization [C]. Proceedings of Neural Information Processing Systems, Cambridge, MA: MIT Press, 2001, (13): 556-562.
[84] Patrik O. Hoyer. Non-negative Matrix Factorization with Sparseness Constraints . Journal of Machine Learning Research 5 (2004) 1457–1469
[85] Rafal Zdunek and Andrzej Cichocki. Nonnegative matrix factorization with constrained second-order optimization.Signal Processing, Volume 87, Issue 8, August 2007, Pages 1904-1916
[86] Michael W. Berry, Murray Browne, Amy N. Langville, V. Paul Pauca and Robert J. Plemmons. Algorithms and applications for approximate nonnegative matrix factorization.Computational Statistics & Data Analysis, Volume 52, Issue 1, 15 September 2007, Pages 155-173
[87]陈启泉,邱文宇,陈维斌.标准正面人脸图象的特征提取.华侨大学学报(自然科学版),2000.10
[88]宋星光,夏利民,赵桂敏.基于LNMF分解的人脸识别.计算机工程与应用,2005.5,42-50
[89] Feng T, Li S Z, Shum H Y, Zhang H J. Local Non-Negative Matrix Factorization as a Visual Representation [C].Proceedings of the 2nd International Conference on Development and Learning (IEEE ICDL.02). Cambridge, USA: IEEE Computer Society, 2002: 178-183.
[90]李江,郁文贤,匡刚要等.基于模糊隶属度函数的主元分析人脸识别算法.计算机工程与科学,2004,26(6):55-57
[91]朱长仁.基于单视图的多姿态人脸识别算法.计算机学报,2003,26(1):104-109
[92] T.A.Runkler、J.C.Bezdek .Function approximation with polynomial membership functions and alternating cluster estimation ,Fuzzy Set and System 101(1999)207-218
[93] Joshua B. Tenenbaum, Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction, Science, 2000, 290(5500):2319-2323
[94] N.Vlassis Y. Motomura and B. Krose. Supervised dimension reduction of intrinsically low-dimensional data. Neural Computation, 2002,14(1):191-215
[95]袁远,季星来,孙之荣等.Isomap在基因表达谱数据聚类分析中的应用.清华大学学报(自然科学版),2004,44(9):1286-1289
[2]张翠平,苏光大.人脸识别技术综述.中国图像图形学报,第5卷(A版)第11期2000,885-894
[3] R.Chellappa, C.L. Wilson, and S. Sirohey. Human and machine recognition of faces: A survey.In: Proc. IEEE, 83:705-741, May 1995
[4] W. Zhao, R. Chellappa, A. Rosenfeld, P.J. Phillips. Face Recognition: A Literature Survey. url="citeseer.nj.nec.com/374297.html"
[5] E. Montseny, J. Frau. Computer vision :specialized processors for real-time image analysis,Workshop proceedings, Barcelona, Spain, September 1991.
[6] M.Bichsel and A. Pentlend, Human face recognition and the face image set’s topology, CVGIP:Image Understanding 59(2), 1994, 254-261.
[7] Roberto Brunelli and Tomaso Poggio Face Recognition: Features versus Template, IEEE Transaction Pattern Analysis and Machine Intelligence Vol. 15 .no.10 pp1042-1052, 1993
[8] Turk M, Pentland A. Eigen-faces for Recognition Journal of cognitive neuroscience, 3(1),pp71-86, 1991
[9] Babak Moghaddam, Chahab Nastar and Alex Pentland, A Bayesian Similarity Measure for Direct Image Matching, International Conference on Pattern Recognition, Vienna, Austria,August 1996
[10] Baback Moghaddam, Wasiuddin Wahid and Alex Pentland. Beyond Eigenfaces: Probabilistic Matching for Face Recognition, the 3rd IEE Int. Con. On Auto. Face- and Gesture- Recognition,Nara, Japan, 1998.4
[11] Zi-Quan Hong. Algebraic feature extraction of image for recognition. Pattern Recognition,24(3), pp211-219, 1991
[12] M.Bichsel and A. Pentlend, Human face recognition and the face image set’s topology, CVGIP:Image Understanding 59(2), 1994, 254-261.
[13] Laurenz Wiskott, jean-Marc Fellous, Norbert Kruger, and Christoph von der Malsburg. Face Recognition by Elastic Graph Matching. In: IEEE Transations on Pattern Analysis and Machine Intelligence, 19(7), 1997, 775—779
[14] A.V.Nefian and M.H.Hayes. Face detection and recognition using Hidden Markov Models in International Conference on Image Processing, 1998
[15] Ming-Hsuan Yang, David Kriegman, and Narendra Ahuja,.Detecting Faces in Images: A Survey, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 24, no.1, pp. 34-58, 2002
[16] F.Sarnaria. Face recognition using Hidden Markov Models. Ph.D thesis, University of Cambridge, 1994
[17] Brunelli R,Poggio T. Face recognition:features versus templates.IEEE Trans. PAMI, 1993. 15(10): 1042-1052
[18] N. Roder, X. Li. Accuracy Analysis for Facial Feature Detection. Pattern Recognition, 1996, 29(1): 143~157
[19] K. M. Lam, H. Yan. Locating and Extracting the Eye in Human Face Images. Pattern Recognition, 1996, 29(5): 143~157
[20] J. Y. Deng, F. Lai. Region-based Template Deformation and Masking for Eye-feature Extraction and Description. Pattern recognition, 1997, 30(3): 403~419
[21] Yuille A L.Deformable templates for face recognition[J].Journal of Cognitive Neuroscience, 1991,3(1): 71-86.
[22] Brunelli R,Poggio T.Face recognition:features versus templates[J].IEEE Transactions on PAMI, 1993, 15(10):1042-1052.
[23] H. Peng, D. Zhang, Dual Eigenspace Method for Human Face Recognition. Electronics Letters, 1997, 33(4): 283~274
[24] D. L. Swets,J. J. Weng, Using Discriminant Eigenfeatures for image retrieval. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1996, 18(8): 831~836
[25] P. N. Belhumeur, J. P. Hespanha, D. J. Kriegman, Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1996, 18(7): 711~720
[26] BARTLETT M S,MOVELLAN J R,SEJNOWSKI T J.Face recognition by independent component analysis.IEEE Transactions on Neural Networks,2002,13(6):1450一1464
[27] BAEK K,DRAPER B A,BEVERIDGE J R.PCA vs.ICA:A comparison on the feret data set. Intelnational Conference on Computer Vision,Pattern Recognition and Image,2002,824一827
[28] Lee D D, Seung H S. Leaming the parts of objects by non-negative matrix factorization [J]. Nature (S0028-0836), 1999, 401(21): 788-791
[29] Turk M, Pentland A. Eigenfaces for Recognition [J]. Journal of Cognitive Neuroscience (S0898-929X), 1991, 3(1): 71-86
[30] Turk M, Pentland A. Face Recognition Using Eigenfaces [C] Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Maui: IEEE, 1991: 586-591
[31] Li S Z, Hou X W, Zhang H J. Learning spatially localized, parts-based representation [C]// Int. Conf. Computer Vision and Pattern Recognition, Washington, USA: IEEE Computer Society, 2001: 207-212
[32] Buciu Ioan, Pitas Ioannis. Application of non negative and local non negative matrix factorization to facial expression recognition [C]. Proceedings of the 17th International Conference on Pattern Recognition (ICPR’04), Washington, USA: IEEE Computer Society, 2004: 288-291
[33] Chen X, Gu L, Li S Z, et al. Learning representative local features for face detection [C]. IEEE Proceedings of Computer Vision and Pattern Recognition, Kauai, USA: IEEE , 2001: 1126-1131
[34] Zafeiriou S, Tefas A, Pitas I. Discriminant NMFfaces for Frontal Face Verification [C]. Machine Learning for Signal Processing (MLSP 2005), Mystic. USA: IEEE, 2005: 355-359.
[35] L.E. Baum, T. Petrie. Statistical Inference for Probabilistic of Finite Markov Chains, Annals of Mathematical Statistics, 1966, 37(12): 1554~1563
[36] L.E. Baum, J. A. Egon. An Inequality with Applications to Statistical Estimation for Probabilistic Functions of a Markov Process and to a Model for Ecology. Bulletin of the American Medical Society, 1967, 73:360~363
[37] L.E. Baum, G. R. Sell, Growth Functions for Translations on Manifolds. Pac. J. Math., 1968, 27(2): 211~227
[38] L.E. Baum, T. Petrie, G. Soules, et al. A Maxinization Technique Occurring in the Statistical Analysis of Probabilistic Function of Markov Chains, Annals of Mathematical Statistics, 1970, 41(1): 164~171
[39] L.E. Baum. An Inequality and Associated Maximization Technique in Statistical Estimation for Probabilistic Functions of Markov Process. Inequalities, 1972, 3(1):1~8
[40] F. Smaria, S. Young, HMM Based Architecture for Face Recognition. Image and Computer Vision, 1994, 12(8): 537~543.
[41] F. Samaria. Face Recognition Using Hidden Markov Models. Ph.D. Fissertation , University of Cambridge, 1994.
[42] A. V. Nefian, M. H. Hayes, A Hidden Markov for face recognition. In proc. ICASSP, 1998, pages: 2721~2724.
[43] R. Brunelli, T. Poggio. Face Recognition: Features Versus Templates. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1993, 15(10): 1042~1052.
[44]李刚,高政.人脸识别理论研究进展.计算机与现代化,2003,3
[45] Lin S H, Kung S Y,Lin J.Face Recognition /Detection by Probabilistic Decision Based Neural Network.IEEE Trans on Neural Networks,1997,8(1):114-134
[46] Nastar C,Ayachc N. Frequency-based nonrigid motion analysis:application to four dimensional medical images. IEEE Trans. PAMI,1996,18(11):1067-1079
[47] M. Lades,J. Vorbuggen, J. Buhmann, et al. Distortion Invariant Object Recognition in the Dynamic Link Architecture. IEEE Trans. On Computers, 1991, 42(3): 300~311.
[48] C. Kotropoulos, I. Pitas. Face authentication based on morphological grid matching. IEEE inter. Conf. on Image Processing, 1997, pages:105~108.
[49] L. Wiskott, J. M. Fellous, N. kruger, et al. Face Recognition by Elastic Bunch Graph Matching. IEEE Trans. on Pattern Analysis and Machine Intelligence, 1997, 19(7): 775~779.
[50] L. Wiskott. Phantom Faces for Face Analysis. Pattern Recognition, 1997, 30(6): 1209~1230
[51] O. Nakamura, S. Mathur, T. Minami. Identification of Human Faces Based on Isodensity Maps. Pattern recognition, 1991, 24(3): 263~272.
[52] P. Kruizinga和N. Petkov. Person Identification Based on Multiscale Matching of Cortical Images. In proc. HPCN Europe, 1995, Pages: 420-427.
[53] Donoho D L,Grimes C. Hessian eigenmaps: New locally linear embedding techniques for high-dimensional data. Proceedings of the National Academy of Sciences,2003,100 (10): 5591-5596
[54] Zhang C S,Wang J,Zhao N Y,et al. Reconstruction and analysis of multi-pose face images based on nonlinear dimensionality reduction. Pattern Recognition,2004,37(l):325-336
[55] Weinberger K Q , Saul L K. Unsupervised learning of image manifolds by semidefinite programming. International Journal of Computer Vision,2006,70(1):77-90
[56] Belkin M,Niyogi P. Semi-Supervised learning on Riemannian manifolds. Machine Learning,2004,56(1): 209-239
[57] Belkin M,Niyogi P,Sindhwani V. On manifold regularization. Proceedings of the International Conference on Aland Statistics,2005:17-24
[58] Roweis S,Saul L,Hinton G. Global coordination of local linear models. Advances in Neural Information Processing Systems,2002:889-896
[59] Brand M. Minimax embeddings. Advances in Neural Information Processing Systems,2004,16:505-512
[60]赵连伟,罗四维,赵艳敞.高维数据的低维嵌入及嵌入维数研究.软件学报,2005,16 (8):1423-1430
[61]何力,张军平,周志平.基于放大因子和延伸方向研究流形学习算法.计算机学报,2005 28(12):2000-2009
[62] Zhang Z,Zha H. Principal manifold sand nonlinear dimension reduction via local tangent space alignment. SIAM Journal of Scientific Computing,2004,26(1):313-338
[63] K. Fukunaga and D. R. Olsen. An algorithm for finding intrinsic dimensionality of data. IEEE Transactions on Computers, 20(2):176–193, 1971
[64] Lawrence K.Saul; Sam T.Roweis; Think Globally, Fit Locally: Unsupervised Learning ofNonlinear Manifolds; Technical Report MS CIS-01-18, Univ. of Pennsylvania 2002
[65] James M.Lattin,J.Douglas Carrol,Paul E.Green,多元数据分析(英文版),机械工业出版社,北京,2003年7月,91-101,211-218
[66]方驰,丁晓青,吴佑寿.基于PCA的脱机手写汉字的统计模型及其应用.模式识别与人工智能,2001,14(1)
[67]徐蓉,姜峰,姚鸿勋.流形学习概述.智能系统学报,2006,1(1):44-51
[68] C.M. Bishop, Neural Networks for Pattern Recognition. Oxford:Clarendon Press,1995
[69] N.Vlassis Y. Motomura and B. Krose. Supervised dimension reduction ofintrinsically low-dimensional data. Neural Computation, 14(1):191-215, January 2002
[70] Joshua B. Tenenbaum et al, A Global Geometric Framework for NonlinearDimensionality Reduction, Science, Vol 290, December 22, 2000.
[71] J. A. Lee, A. Lendasse and M. Verleysen. Curvilinear Distance Analysis versus Isomap ESANN 2002 proceedings - European Symposium on Artificial Neural Networks , Bruges (Belgium), 24-26 April 2002, 185-192.
[72] Sam T. Roweis et al, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, Vol 290, December 22, 2000
[73] L. K. Saul and S. T. Roweis. Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds. Journal of Machine Learning Research, v4, pp.119-155, 2003.
[74] Zhenyue Zhang and Hongyuan Zha. Local Linear Smoothing for NonlinearManifold Learning. CSE-03-003, Technical Report, CSE, Penn State Univ., 2003.
[75] de Ritter D, Kouropteva O, Okun O, Pietik?inen M & Duin RPW. Supervised locally linear embedding. Artificial Neural Networks and Neural Information Processing, ICANN/ICONIP 2003 Proceedings, Lecture Notes in Computer Science 2714, Springer, 333-341
[76] V. de Silva, J. B. Tenenbaum, Global versus local methods in nonlinear dimensionality reduction. (2002), Advances in Neural Information Processing Systems 15. M.SBecker, S., Thrun, S., and Obemayer, K. (eds). Cambridge, MITPress, 2002, 705-712
[77]侯越先.基于自组织的鲁棒非线性维数约减算法.计算机研究与发展,2005.2
[78] J. B. Kruskal.Multidimensional scaling by optimizing goodness of fit to a non-metric hypothesis. Psychometrika, 1964,29(1):1-27
[79] J. B. Tenenbaum, V. de Silva, J. C. Langford . A global geometric framework for nonlinear dimensionality reduction. Science 290 (5500): 2319-2323, 22 December 2000
[80] M.H.C. Law,N. Zhang,A.K. Jain. Nonlinear manifold learning for data stream.Proceedings of SIAM Data Mining,Florida,USA,2004,33~44
[81] Lukui Shi,Pilian He,Enhai Liu. An incremental nonlinear dimensionality reduction algorithm based on ISOMAP. Proceedings of AI05,LNAI 3809,Sydney,2005,892~895
[82] Daniel D. Lee,H, Sebastian Seung. Learning the partsof objects by non-negative matrix factorization, Nature ,401,788-791,1999
[83] Lee D D, Seung H S. Algorithms for non-negative matrix factorization [C]. Proceedings of Neural Information Processing Systems, Cambridge, MA: MIT Press, 2001, (13): 556-562.
[84] Patrik O. Hoyer. Non-negative Matrix Factorization with Sparseness Constraints . Journal of Machine Learning Research 5 (2004) 1457–1469
[85] Rafal Zdunek and Andrzej Cichocki. Nonnegative matrix factorization with constrained second-order optimization.Signal Processing, Volume 87, Issue 8, August 2007, Pages 1904-1916
[86] Michael W. Berry, Murray Browne, Amy N. Langville, V. Paul Pauca and Robert J. Plemmons. Algorithms and applications for approximate nonnegative matrix factorization.Computational Statistics & Data Analysis, Volume 52, Issue 1, 15 September 2007, Pages 155-173
[87]陈启泉,邱文宇,陈维斌.标准正面人脸图象的特征提取.华侨大学学报(自然科学版),2000.10
[88]宋星光,夏利民,赵桂敏.基于LNMF分解的人脸识别.计算机工程与应用,2005.5,42-50
[89] Feng T, Li S Z, Shum H Y, Zhang H J. Local Non-Negative Matrix Factorization as a Visual Representation [C].Proceedings of the 2nd International Conference on Development and Learning (IEEE ICDL.02). Cambridge, USA: IEEE Computer Society, 2002: 178-183.
[90]李江,郁文贤,匡刚要等.基于模糊隶属度函数的主元分析人脸识别算法.计算机工程与科学,2004,26(6):55-57
[91]朱长仁.基于单视图的多姿态人脸识别算法.计算机学报,2003,26(1):104-109
[92] T.A.Runkler、J.C.Bezdek .Function approximation with polynomial membership functions and alternating cluster estimation ,Fuzzy Set and System 101(1999)207-218
[93] Joshua B. Tenenbaum, Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction, Science, 2000, 290(5500):2319-2323
[94] N.Vlassis Y. Motomura and B. Krose. Supervised dimension reduction of intrinsically low-dimensional data. Neural Computation, 2002,14(1):191-215
[95]袁远,季星来,孙之荣等.Isomap在基因表达谱数据聚类分析中的应用.清华大学学报(自然科学版),2004,44(9):1286-1289
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