[1]谢文浩,翟素兰. 基于加权稀疏近邻表示的人脸识别[J].计算机技术与发展,2016,26(02):22-25.
　XIE Wen-hao,ZHAI Su-lan. Face Recognition Based on Weighted Sparse Neighbor Representation[J].,2016,26(02):22-25.
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 基于加权稀疏近邻表示的人脸识别()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年02期
页码:
22-25
栏目:
智能、算法、系统工程
出版日期:
2016-02-10

文章信息/Info

Title:
 Face Recognition Based on Weighted Sparse Neighbor Representation
文章编号:
1673-629X（2016)02-0022-04
作者:
 谢文浩; 翟素兰
 安徽大学 数学科学学院
Author(s):
 XIE Wen-hao; ZHAI Su-lan
关键词:
 ; 稀疏表示; 特征提取; 加权近邻; 人脸识别
Keywords:
 ; sparse representation; feature extraction; weighted nearest neighbor; face recognition
分类号:
TP391.4
文献标志码:
A
摘要:
 稀疏表示的人脸识别目前受到广泛的关注。针对现有稀疏近邻表示算法没有考虑不同训练样本对测试样本的重构权重，同时为了提高基于稀疏近邻表示人脸识别的识别率，文中提出一种加权稀疏近邻表示的人脸识别算法。首先在每一类训练样本中寻找与测试样本最近的k个样本，构成这一类新的训练样本；然后在每一类中都进行同样的操作，从而构造一个新的训练字典，在求解l1范数最小化的稀疏系数时，为每一个新的训练样本对应的稀疏系数赋上一个权值；最后在新的字典下，根据重构误差最小化来完成识别任务。在Yale B数据库和ORL数据库上的大量实验结果表明，文中所提算法与KNN算法和稀疏近邻表示算法相比，取得了较高的识别率，证明了该方法的有效性。
Abstract:
 Currently,face recognition via sparse representation has gained widespread attention. Since the sparse neighbor representation algorithm without considering the different weight of training samples to reconstruct the test sample,simultaneously,to improve the recog-nition rate of face recognition based on sparse neighbor representation, in this paper, a face recognition algorithm of weighted sparse neighbor representation was proposed. First,in each class of the training samples, k samples nearest to the test samples are selected,con-structed new training samples in this class. And then do the same operation in each class,so as to construct a new training dictionary, when solving sparse coefficient with l1 norm minimization,a weight is given to the sparse coefficient of each new training sample. Finally with the new training dictionary,according to the minimum reconstruction error to complete the recognition task. The most experiments results on Yale B face database and ORL face database show that the proposed method achieves higher recognition rate compared with KNN and SNRC ( Sparse Neighbor Representation for Classification) ,which confirms the effectiveness of the algorithm.

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更新日期/Last Update: 2016-04-14