计算机科学 ›› 2015, Vol. 42 ›› Issue (3): 274-279.doi: 10.11896/j.issn.1002-137X.2015.03.057

• 图形图像与模式识别 • 上一篇    下一篇

分块多线性主成分分析及其在人脸识别中的应用研究

谢 佩,吴小俊   

  1. 江南大学物联网工程学院 无锡214122,江南大学物联网工程学院 无锡214122
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金(61373055)资助

Modular Multilinear Principal Component Analysis and Application in Face Recognition

XIE Pei and WU Xiao-jun   

  • Online:2018-11-14 Published:2018-11-14

摘要: 主成分分析(Principal Component Analysis,PCA)是人脸识别中一个经典的算法,但PCA方法在特征提取时考虑的是图像的整体信息,并没有考虑图像的局部信息,而分块PCA(Modular Principal Component Analysis,Modular PCA)则可以有效地提取图像中重要的局部信息,所以在人脸识别实验中获得了比传统PCA更好的识别效果。但PCA和Modular PCA都要进行图像的矢量化,这会破坏原始数据的空间结构,也有可能会导致“维数灾难”。多线性主成分分析(Multilinear Principal Component Analysis,Multilinear PCA)作为PCA在高维数据上的扩展,直接使用矩阵或者高阶的张量来获得有效特征,既可以避免“维数灾难”,又可以体现直接将张量数据作为处理对象时保留原始数据较好基本结构信息的优点。在研究Modular PCA和Multilinear PCA的基础上,提出了分块多线性主成分分析(Modular Multilinear Principal Component Analysis,M2PCA)算法,用于识别人脸。在Yale、XM2VTS和JAFFE人脸数据库上进行了人脸识别实验,结果表明,在同等的分块条件下,所提出的方法的识别效果要优于Modular PCA的方法。

关键词: 人脸识别,特征提取,Multilinear PCA,Modular PCA

Abstract: Though principal component analysis (PCA) is a classical method for face recognition,the PCA method extracts global features of the original images,and it does not consider the local discriminant features.In contrast,Modular PCA method extracts the important local discriminant features,and it achieves better performance than the PCA method in face recognition.However,vectorization in PCA or modular PCA often causes "curse of dimensionality".In order to extract features from matrix or higher-order tensor objects directly,multilinear principal component analysis (Multilinear PCA) is developed.Multilinear PCA can avoid "curse of dimensionality",meanwhile it will not destroy the original data structure.Inspired by Modular PCA and Multilinear PCA,we proposed a new method called modular multilinear principal component analysis (M2PCA) for face recognition.Experiments were conducted on the Yale,XM2VTS and JAFFE databases respectively,and experimental results indicate that,under the same condition of sub-blocks,the proposed method is obviously superior to the general Modular PCA.

Key words: Face recognition,Feature extraction,Multilinear PCA,Modular PCA

[1] Jolliffe I.Principal component analysis[M].John Wiley & Sons,Ltd,2005
[2] Kirby M,Sirovich L.Application of the Karhunen-Loeve procedure for the characterization of human faces[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1990,12(1):103-108
[3] Turk M A,Pentland A P.Face recognition using eigenfaces[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1991(Proceedings CVPR’91).IEEE,1991:586-591
[4] Gottumukkal R,Asari V K.An improved face recognition technique based on modular PCA approach[J].Pattern Recognition Letters,2004,25(4):429-436
[5] 陈伏兵,谢永华,严云洋,等.分块 PCA 鉴别特征抽取能力的分析研究[J].计算机科学,2006,33(3):155-159
[6] Yang J,Zhang D,Frangi A F,et al.Two-dimensional PCA:anew approach to appearance-based face representation and re-cognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(1):131-137
[7] Li M,Yuan B.2D-LDA:A statistical linear discriminant analysis for image matrix[J].Pattern Recognition Letters,2005,26(5):527-532
[8] Chen S,Zhao H,Kong M,et al.2D-LPP:a two-dimensional extension of locality preserving projections[J].Neurocomputing,2007,70(4):912-921
[9] Li Z,Du M.2D-NPP:An Extension of Neighborhood Preserving Projection[C]∥2007 International Conference on ComputationalIntelligence and Security.IEEE,2007:410-414
[10] Zhang D,Zhou Z H.(2D)2PCA:Two-directional two-dimensional PCA for efficient face representation and recognition[J].Neurocomputing,2005,69(1):224-231
[11] Noushath S,Hemantha Kumar G,Shivakumara P.(2D) 2 LDA:An efficient approach for face recognition[J].Pattern Recognition,2006,39(7):1396-1400
[12] Nagabhushan P,Guru D S,Shekar B H.(2D) 2FLD:An efficient approach for appearance based object recognition[J].Neurocomputing,2006,69(7):934-940
[13] Vasilescu M A O,Terzopoulos D.Multilinear subspace analysis of image ensembles[C]∥2003 IEEE Computer Society Confe-rence on Computer Vision and Pattern Recognition,2003.IEEE,2003,2:II-93
[14] Lu H,Plataniotis K N,Venetsanopoulos A N.MPCA:Multilinear principal component analysis of tensor objects[J].IEEE Transactions on Neural Networks,2008,19(1):18-39
[15] Lu H,Plataniotis K N,Venetsanopoulos A N.Uncorrelatedmultilinear principal component analysis for unsupervised multilinear subspace learning[J].IEEE Transactions on Neural Networks,2009,20(11):1820-1836
[16] Yan S,Xu D,Yang Q,et al.Multilinear discriminant analysis for face recognition[J].IEEE Transactions on Image Processing,2007,16(1):212-220
[17] Han Xian-hua,Chen Yen-Wei.Multilinear supervised neighborhood embedding with local descriptor tensor for face recognition[J].IEICE transactions on information and systems,2011,94(1):158-161
[18] Tao D,Li X,Wu X,et al.General tensor discriminant analysis and gabor features for gait recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2007,29(10):1700-1715
[19] Yan S,Xu D,Yang Q,et al.Discriminant analysis with tensor representation[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2005(CVPR 2005).IEEE,2005,1:526-532
[20] Mohamad AL-Shiha A A,Woo W L,Dlay S S.Multi-linearneighborhood preserving projection for face recognition[J].Pattern Recognition,2014,47(2):544-555
[21] Kolda T G,Bader B W.Tensor decompositions and applications[J].SIAM review,2009,51(3):455-500
[22] http://www.cvc.yale.edu/projects/ yalefaces/yalefaces.html
[23] Messer K,Matas J,Kittler J,et al.XM2VTSDB:The extended M2VTS database[C]∥Second international conference on audio and video-based biometric person authentication.1999,964:965-966
[24] Lyons M J,Budynek J,Akamatsu S.Automatic classification ofsingle facial images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1999,21(12):1357-1362

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