# [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] 点击复制 一种正交局部鉴别嵌入的人脸识别算法() 分享到： var jiathis_config = { data_track_clickback: true };

43

2013年第6期

1208-1211

2013-11-20

## 文章信息/Info

Title:
Orthogonal local discriminant embedding for face recognition

Author(s):
Huang Bei
School of Information Science and Engineering, Southeast University, Nanjing 210096, China

Keywords:

TP391
DOI:
10.3969/j.issn.1001-0505.2013.06.014

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.

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