[1]王园萍,殷洪友. 基于矩阵分数范数的人脸识别方法[J].计算机技术与发展,2015,25(04):22-25.
　WANG Yuan-ping,YIN Hong-you. Face Recognition Method Based on Fractional Matrix Norm[J].,2015,25(04):22-25.
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 基于矩阵分数范数的人脸识别方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年04期
页码:
22-25
栏目:
智能、算法、系统工程
出版日期:
2015-04-10

文章信息/Info

Title:
 Face Recognition Method Based on Fractional Matrix Norm
文章编号:
1673-629X（2015)04-0022-04
作者:
 王园萍; 殷洪友
 南京航空航天大学 理学院
Author(s):
 WANG Yuan-ping; YIN Hong-you
关键词:
 ; 特征选择; 矩阵范数; 稀疏性; 人脸识别
Keywords:
 ; feature selection; fractional matrix norm; sparsity; face recognition
分类号:
TP301
文献标志码:
A
摘要:
 近年来,混合的分数矩阵范数l2,p(0< p≤1)在高维数据处理的特征选取中有很好的表现,其基本思想是利用了欧氏范数l2的光滑性和分数范数lp(0< p≤1)的稀疏性。大量实验数据表明,混合分数矩阵范数l2,p(0< p <1)不仅比传统的向量范数l1具有更好的联合稀疏性,对噪声的抗干扰性也更强。文中依据人脸数据的稀疏结构,建立基于混合矩阵范数l2,p(0< p≤1)极小化的特征选取模型,结合最近邻识别方法,提出了一类新的鲁棒人脸分类方法。在多个人脸数据集上的实验结果表明,基于分数矩阵范数的新模型比传统的人脸识别方法有更好的特征选择及分类效果。
Abstract:
 Recently,mixed fractional matrix norm l2,p(0 < p≤1) has exhibited good performance for feature selection in high-dimen-sional data processing. The natural idea is combining the smoothness of Euclid norm l2 and the sparsity of norm lp(0 < p≤1) . A variety of experimental results have showed that the fractional matrix norm l2,p(0 < p < 1) has better joint sparsity and stronger reliability than traditional vector norm l1 . In this paper,a l2,p(0 < p≤1) -based minimization is presented to select feature in face data. Combined with the nearest neighbor classification method,a robust face recognition method is proposed. The extensive experiments on face data sets have showed that the l2,p(0 < p < 1) -minimization model performs better than the state-of-the-art on feature selection and classification effect.

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更新日期/Last Update: 2015-06-02