Face Recognition Based on Wavelet Kernel Non-Negative Matrix Factorization

被引：3
|
作者：
Bai, Lin [1 ]
Li, Yanbo [1 ]
Hui, Meng [1 ]
机构：
[1] Changan Univ, Sch Elect & Control Engn, Xian 710064, Shaanxi, Peoples R China
来源：
CYBERNETICS AND INFORMATION TECHNOLOGIES | 2014年 / 14卷 / 03期
关键词：
Face recognition; non-negative matrix factorization; RBF network; kernel method;
D O I：
10.2478/cait-2014-0031
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper a novel face recognition algorithm, based on wavelet kernel non-negative matrix factorization (WKNMF), is proposed. By utilizing features from multi-resolution analysis, the nonlinear mapping capability of kernel nonnegative matrix factorization could be improved by the method proposed. The proposed face recognition method combines wavelet kernel non-negative matrix factorization and RBF network. Extensive experimental results on ORL and YALE face database show that the suggested method possesses much stronger analysis capability than the comparative methods. Compared with PCA, non-negative matrix factorization, kernel PCA and independent component analysis, the proposed face recognition method with WKNMF and RBF achieves over 10 % improvement in recognition accuracy.
引用
收藏
页码：37 / 45
页数：9
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