[1]黄蓓.一种正交局部鉴别嵌入的人脸识别算法[J].东南大学学报(自然科学版),2013,43(6):1208-1211.[doi:10.3969/j.issn.1001-0505.2013.06.014]
 Huang Bei.Orthogonal local discriminant embedding for face recognition[J].Journal of Southeast University (Natural Science Edition),2013,43(6):1208-1211.[doi:10.3969/j.issn.1001-0505.2013.06.014]
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一种正交局部鉴别嵌入的人脸识别算法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
43
期数:
2013年第6期
页码:
1208-1211
栏目:
计算机科学与工程
出版日期:
2013-11-20

文章信息/Info

Title:
Orthogonal local discriminant embedding for face recognition
作者:
黄蓓
东南大学信息科学与工程学院, 南京 210096
Author(s):
Huang Bei
School of Information Science and Engineering, Southeast University, Nanjing 210096, China
关键词:
人脸识别 局部鉴别嵌入 谱回归 正交化
Keywords:
face recognition local discriminant embedding spectral regression orthogonalization
分类号:
TP391
DOI:
10.3969/j.issn.1001-0505.2013.06.014
摘要:
为了解决局部鉴别嵌入(LDE)算法的高维小样本泛化能力弱和分解致密矩阵计算量较大的问题,提出了一种基于谱回归的正交局部鉴别嵌入算法(SR-OLDE),采用谱回归理论与正交化技术相结合的方法,将投影函数的求解转化为回归问题的求解.该算法首先计算训练样本的特征向量; 然后通过回归方法计算投影向量,得到测试数据集,从而将n×n维的致密矩阵的特征分解转化为m×m维矩阵的特征分解,n,m分别为人脸特征矩阵维数和人脸样本数; 最后对投影向量进行Gram-Schmidt正交化,得到正交的投影矩阵,从而可准确估计高维数据的内在维数,提高了样本的泛化能力.实验结果表明,该算法在降低人脸特征矩阵维数和提高人脸识别率的同时,缩短了计算时间.
Abstract:
The spectral regression-based orthogonal local discriminant embedding(SR-OLDE)algorithm is proposed to improve the generalization performance of high-dimensional small samples and the efficiency of decomposing dense matrix in the local discriminant embedding(LDE)algorithm. The projection function is transformed into the regression problem by using the spectral regression theory and orthogonalization technology. First, the eigen vector of the training samples is calculated. And then in order to obtain the test data sets, the projection vector is calculated through the regression method. Thereby the eigen decomposition of n×n dimensional dense matrix is transferred into that of m×m dimensional matrix, where n is the dimension of eigenface matrix and m is the number of face samples. Finally, the projection vector is orthogonalized by Gram-Schmidt method to obtain the orthogonal projection matrix, which can accurately estimate the intrinsic dimension of high-dimensional data and improve the generalization performance of the sample. The experiments show that the SR-OLDE algorithm has better performance in reducing dimensions of eigenface matrix and recognition rate than the LDE algorithm, and its computation time is decreased.

参考文献/References:

[1] Wiskott L, Fellous J, Kruger N, et al. Face recognition by elastic bunch graph matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 775-779.
[2] Kumar P P, Vadakkepat P, Loh A P. Graph matching based hand posture recognition using neuro-biologically inspired features[C]//11th International Conference on Control Automation Robotics and Vision. Singapore, 2010:1151-1156.
[3] Kshirsagar V P, Baviskar M R, Gaikwad M E. Face recognition using Eigenfaces[C]//3rd International Conference on Computer Research and Development. Shanghai, China, 2011:302-306.
[4] Huang S M, Yang J F. Subface hidden Markov models coupled with a universal occlusion model for partially occluded face recognition[J]. IET Biometrics, 2012,1(3):149-159.
[5] Du S, Shehata M, Badawy W. A novel algorithm for illumination invariant DCT-based face recognition[C]//25th IEEE Canadian Conference on Electrical and Computer Engineering. Montreal, QC, Canada, 2012:1-4.
[6] Maria D M, Michele N, Daniel R, et al. Robust face recognition for uncontrolled pose and illumination changes[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2013, 43(1):149-163.
[7] He Y, Jin B, Yang S. Improving BP neural network for the recognition of face direction[C]//International Symposium on Computer Science and Society. Kota Kinabalu, Malaysia, 2011:79-82.
[8] Jing X Y, Sun J, Yao Y F, et al. Supervised and unsupervised face recognition method based on 3CCA[C]//International Conference on Automatic Control and Artificial Intelligence. Xiamen, China, 2012:2009-2012.
[9] Seung H S, Lee D D. The manifold ways of perception[J]. Science, 2000, 290(5500): 2268-2269.
[10] Tenenbaum J B, de Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction[J]. Science, 2000, 290(5500): 2319-2323.
[11] Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000, 290(5500):2323-2326.
[12] He X F, Yan S C, Hu Y X, et al. Face recognition using Laplacianfaces[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(3):328-340.
[13] Brenda F K, Mark J B, Joshua C K, et al. Face recognition performance: role of demographic information[J].IEEE Transactions on Information Forensics and Security, 2012, 7(6):1789-1801.
[14] Chen H T,Chang H W, Liu T L.Local discriminant embedding and its variants[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA, 2005: 846-853.

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备注/Memo

备注/Memo:
作者简介: 黄蓓(1970—),女,硕士,讲师,huangbei-seu@seu.edu.cn.
基金项目: 国家高技术研究发展计划(863计划)资助项目(2013AA014001).
引文格式: 黄蓓.一种正交局部鉴别嵌入的人脸识别算法[J].东南大学学报:自然科学版,2013,43(6):1208-1211. [doi:10.3969/j.issn.1001-0505.2013.06.014]
更新日期/Last Update: 2013-11-20