基于补集零空间与最近空间距离的人脸识别

doi:10.11772/j.issn.1001-9081.2017.05.1475

计算机应用 ›› 2017, Vol. 37 ›› Issue (5): 1475-1480.DOI: 10.11772/j.issn.1001-9081.2017.05.1475

• 计算机视觉与虚拟现实 • 上一篇下一篇

基于补集零空间与最近空间距离的人脸识别

原豪杰, 孙桂玲, 许依, 郑博文

南开大学电子信息与光学工程学院, 天津 300350

收稿日期:2016-07-28 修回日期:2016-10-17 出版日期:2017-05-10 发布日期:2017-05-16
通讯作者: 孙桂玲
作者简介:原豪杰(1992-),男,山西长治人,硕士研究生,主要研究方向:模式识别、无线传感器网络、压缩传感;孙桂玲(1964-),女,天津人,教授,博士,主要研究方向:无线传感器网络、模式识别、信号与信息处理;许依(1992-),女,湖南衡阳人,博士研究生,主要研究方向:压缩传感、模式识别、无线传感器网络;郑博文(1993-),男,河北石家庄人,硕士研究生,主要研究方向:信息监测与智能控制系统、压缩传感、模式识别。
基金资助:
高等学校博士学科点专项科研基金资助项目（20130031110032）；天津市光电传感器与传感器网络技术重点实验室开放课题资助项目。

Face recognition based on complement null-space and nearest space distance

YUAN Haojie, SUN Guiling, XU Yi, ZHENG Bowen

College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China

Received:2016-07-28 Revised:2016-10-17 Online:2017-05-10 Published:2017-05-16
Supported by:
This work is partially supported by the Specialized Research Fund for the Doctoral Program of Higher Education (20130031110032), the Tianjin Key Laboratory of Photoelectric Sensor & Sensor Network Technology.

摘要/Abstract

摘要： 针对人脸识别中在分类器判别时没有充分利用类间差异的问题，提出一种补集零空间（CNS）算法，并进一步提出结合CNS算法与最近空间距离的人脸识别算法——补集零空间与最近空间距离算法（CNSD）。首先，在训练样本中，对每一种类别的人脸样本，构建其子空间并计算其补集的零空间；其次，计算测试样本与所有子空间和补集零空间的距离，找到最小的子空间距离与最大的补集零空间距离对应的类别，将其判别为测试样本的类别。算法在ORL与AR人脸数据集上进行了测试，当训练样本数较小时，CNS算法与CNSD算法识别率远高于最近邻分类器（NN）算法、最近空间距离（NS）算法、最近最远空间距离（NFS）算法；训练样本数较大时，CNS算法与CNSD算法识别率也略高于NN算法、NS算法、NFS算法。实验结果表明，所提算法能充分利用图像的类间差异，提高人脸识别的成功率。

关键词: 人脸识别, 补集零空间, 最近空间距离, 分类器, 子空间

Abstract: In order to solve the problem that classifiers do not make full use of the differences between different types of face samples in face recognition, an effective method for face recognition was proposed, namely Complement Null-Space (CNS) algorithm; and further more, another method which combined CNS and nearest space Distance (CNSD) was proposed. Firstly, subspaces and complement null-spaces of all types of training images were constructed. Secondly, the distances between the test image and all types of subspaces as well as the distances between the test image and all types of complement null-spaces were calculated. Finally, the test image was classified into the type which has the minimum subspace distance and the maximum complement null-space distance. On ORL and AR face databases, the recognition rates of CNS and CNSD are much higher than those of Nearest Neighbor (NN), Nearest Space (NS) method and Nearest-Farthest Subspace (NFS) method when the number of training samples is small; and it is a little higher than that of NN, NS and NFS when dealing with large samples. Simulation results show that the proposed algorithm can make full use of the differences between different types of images and has good recognition ability.

Key words: face recognition, Complement Null-Space (CNS), nearest space distance, classifier, subspace

中图分类号:

TP391.4

原豪杰, 孙桂玲, 许依, 郑博文. 基于补集零空间与最近空间距离的人脸识别[J]. 计算机应用, 2017, 37(5): 1475-1480.

YUAN Haojie, SUN Guiling, XU Yi, ZHENG Bowen. Face recognition based on complement null-space and nearest space distance[J]. Journal of Computer Applications, 2017, 37(5): 1475-1480.

参考文献

[1] SIROVICH L, KIRBY M. Low-dimensional procedure for the characterization of human faces[J]. Journal of the Optical Society of America A:Optics and Image Science, 1987, 33(4):591-596.
[2] LIU R, ZHANG J, WANG L, et al. Research on face recognition method based on combination of SVM and LDA-PCA[C]//Proceedings of the 3nd International Conference on Communications, Signal Processing, and Systems. Berlin:Springer, 2014:101-109.
[3] WRIGHT J, YANG A Y, GANESH A, et al. Robust face recognition via sparse representation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2009, 31(2):210-227.
[4] LI C, LIU J, WANG A, et al. Matrix reduction based on generalized PCA method in face recognition[C]//Proceedings of the 20145th International Conference on Digital Home. Piscataway, NJ:IEEE, 2014:35-38.
[5] YANG J, ZHANG D, FRANGI A F, et al. Two-dimensional PCA:a new approach to appearance-based face representation and recognition[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2004, 26(1):131-137.
[6] ZUO W, ZHANG D, WANG K. Bidirectional PCA with assembled matrix distance metric for image recognition[J]. IEEE Transactions on Systems Man & Cybernetics Part B:Cybernetics, 2006, 36(4):863-872.
[7] ZHOU C, WANG L, ZHANG Q, et al. Face recognition based on PCA and logistic regression analysis[J]. Optik-International Journal for Light and Electron Optics, 2014, 125(20):5916-5919.
[8] TIAN X, TIAN M. Face recognition using a hybrid algorithm based on improved PCA[C/OL].[2016-10-20].http://www.mendeley.com/research/face-recognition-using-hybrid-algorithm-based-improved-pca/.
[9] LUAN X, FANG B, LIU L, et al. Extracting sparse error of robust PCA for face recognition in the presence of varying illumination and occlusion[J]. Pattern Recognition, 2014, 47(2):495-508.
[10] MI J X, HUANG D S, WANG B, et al. The nearest-farthest subspace classification for face recognition[J]. Neurocomputing, 2013, 113(7):241-250.
[11] CHOI L U, MURCH R D. A transmit preprocessing technique for multiuser MIMO systems using a decomposition approach[J]. IEEE Transactions on Wireless Communications, 2004, 3(1):20-24.
[12] ZU K, DE LAMARE R C, HAARDT M. Generalized design of low-complexity block diagonalization type precoding algorithms for multiuser MIMO systems[J]. IEEE Transactions on Communications, 2013, 61(10):4232-4242.
[13] LI S Z, LU J. Face recognition using the nearest feature line method[J]. IEEE Transactions on Neural Networks, 1999, 10(2):439-443.
[14] CHIEN J T, WU C C. Discriminant waveletfaces and nearest feature classifiers for face recognition[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2003, 24(12):1644-1649.
[15] SHI Q, ERIKSSON A, HENGEL A V D, et al. Is face recognition really a compressive sensing problem?[C]//Proceedings of the 2011 IEEE Conference on Computer Vision & Pattern Recognition. Piscataway, NJ:IEEE, 2011:553-560.

基于补集零空间与最近空间距离的人脸识别

Face recognition based on complement null-space and nearest space distance

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