Full Text:   <2184>

Summary:  <1877>

CLC number: TP391.4

On-line Access: 2016-03-07

Received: 2015-08-07

Revision Accepted: 2015-12-02

Crosschecked: 2016-01-20

Cited: 0

Clicked: 6304

Citations:  Bibtex RefMan EndNote GB/T7714


Xiao-hu Ma


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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.3 P.212-223


Local uncorrelated local discriminant embedding for face recognition

Author(s):  Xiao-hu Ma, Meng Yang, Zhao Zhang

Affiliation(s):  School of Computer Science and Technology, Soochow University, Suzhou 215006, China; more

Corresponding email(s):   xhma@suda.edu.cn, eyangmeng@163.com, cszzhang@suda.edu.cn

Key Words:  Feature extraction, Local discriminant embedding, Local uncorrelated criterion, Face recognition

Xiao-hu Ma, Meng Yang, Zhao Zhang. Local uncorrelated local discriminant embedding for face recognition[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(3): 212-223.

@article{title="Local uncorrelated local discriminant embedding for face recognition",
author="Xiao-hu Ma, Meng Yang, Zhao Zhang",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%T Local uncorrelated local discriminant embedding for face recognition
%A Xiao-hu Ma
%A Meng Yang
%A Zhao Zhang
%J Frontiers of Information Technology & Electronic Engineering
%V 17
%N 3
%P 212-223
%@ 2095-9184
%D 2016
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500255

T1 - Local uncorrelated local discriminant embedding for face recognition
A1 - Xiao-hu Ma
A1 - Meng Yang
A1 - Zhao Zhang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 17
IS - 3
SP - 212
EP - 223
%@ 2095-9184
Y1 - 2016
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1500255

The feature extraction algorithm plays an important role in face recognition. However, the extracted features also have overlapping discriminant information. A property of the statistical uncorrelated criterion is that it eliminates the redundancy among the extracted discriminant features, while many algorithms generally ignore this property. In this paper, we introduce a novel feature extraction method called local uncorrelated local discriminant embedding (LULDE). The proposed approach can be seen as an extension of a local discriminant embedding (LDE) framework in three ways. First, a new local statistical uncorrelated criterion is proposed, which effectively captures the local information of interclass and intraclass. Second, we reconstruct the affinity matrices of an intrinsic graph and a penalty graph, which are mentioned in LDE to enhance the discriminant property. Finally, it overcomes the small-sample-size problem without using principal component analysis to preprocess the original data, which avoids losing some discriminant information. Experimental results on Yale, ORL, Extended Yale B, and FERET databases demonstrate that LULDE outperforms LDE and other representative uncorrelated feature extraction methods.

This paper proposed a local uncorrelated local discriminant embedding, coined as LULDE. Generally, this is a relatively good paper.


目的:统计不相关是一种重要的性质,然而一些人脸识别算法常将这一性质忽略。统计不相关准则目的是使得特征线性不相关,消除提取的判别特征之间的冗余信息。已有的一些算法只是分别考虑数据集的全局统计不相关特征和数据集的局部的不相关特性。为解决这一问题,本文提出一种新的特征提取算法—局部不相关的局部判别嵌入算法(local uncorrelated local discriminant embedding,LULDE),该算法能同时考虑数据集中的同类和异类样本点的局部信息。
结论:在Yale,ORL,Extended Yale B和FERET四个常用人脸数据库上的大量实验结果表明了本算法的有效性。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Belhumeur, P.N., Hespanha, J.P., Kriegman, D., 1997. Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Patt. Anal. Mach. Intell., 19(7):711-720.

[2]Belkin, M., Niyogi, P., 2003. Laplacian eigenmaps for dimensionality reduction and data representation. Neur. Comput., 15(6):1373-1396.

[3]Chen, H.T., Chang, H.W., Liu, T.L., 2005. Local discriminant embedding and its variants. Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, p.846-853.

[4]Chen, Y., Zheng, W.S., Xu, X.H., et al., 2013. Discriminant subspace learning constrained by locally statistical uncorrelation for face recognition. Neur. Netw., 42:28-43.

[5]Fan, Z.Z., Xu, Y., Zhang, D., 2011. Local linear discriminant analysis framework using sample neighbors. IEEE Trans. Neur. Netw., 22(7):1119-1132.

[6]He, X.F., Niyogi, P., 2003. Locality preserving projections. Proc. Advances in Neural Information Processing Systems, p.327-334.

[7]Jin, Z., Yang, J.Y., Hu, Z.S., et al., 2001. Face recognition based on the uncorrelated discriminant transformation. Patt. Recog., 34(7):1405-1416.

[8]Jing, X.Y., Zhang, D., Jin, Z., 2003. UODV: improved algorithm and generalized theory. Patt. Recog., 36(11):2593-2602.

[9]Jing, X.Y., Li, S., Zhang, D., et al., 2011. Face recognition based on local uncorrelated and weighted global uncorrelated discriminant transforms. Proc. 18th IEEE Int. Conf. on Image Processing, p.3049-3052.

[10]Roweis, S.T., Saul, L.K., 2000. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323-2326.

[11]Tenenbaum, J.B., de Silva, V., Langford, J.C., 2000. A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500):2319-2323.

[12]Turk, M., Pentland, A., 1991. Eigenfaces for recognition. J. Cogn. Neurosci., 3(1):71-86.

[13]Wong, W.K., Zhao, H.T., 2012. Supervised optimal locality preserving projection. Patt. Recog., 45(1):186-197.

[14]Yan, S.C., Xu, D., Zhang, B.Y., et al., 2007. Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Patt. Anal. Mach. Intell., 29(1):40-51.

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