一种快速的基于稀疏表示的人脸识别算法  

A Fast Face Recognition Algorithm Based on Sparse Representation

作　　者：龙法宁[1] 杨夏妮[1] 

机构地区：[1]玉林师范学院计算机科学与工程学院,广西玉林537000

出　　处：《图学学报》2014年第6期889-892,共4页Journal of Graphics

基　　金：广西教育厅科研立项项目桂教科研〔2011〕14号文件资助项目(201106LX512);玉林师范学院青年科研资助项目(2010YJQN19)

摘　　要：基于稀疏表示的人脸识别算法(SRC)识别率相当高,但是当使用l1范数求最优的稀疏表示时,大大增加了算法的计算复杂度,矩阵随着维度的增加,计算时间呈几何级别上升,该文提出利用拉格朗日算法求解矩阵的逆的推导思路,用一种简化的伪逆求解方法来代替l1范数的计算,可将运算量较高的矩阵求逆运算转变为轻量级向量矩阵运算,基于AR人脸库的实验证明,维度高的时候识别率高达97%,同时,计算复杂度和开销比SRC算法大幅度降低95%。As a recently proposed technique, sparse representation based classification（SRC） has been widely used for face recognition（FR）. Sparse representation based SRC algorithm has a high recognition rate. While l1-minimization（l1-min） has recently been studied extensively in optimization, the high computational cost associated with the traditional algorithms has largely hindered their application to high-dimensional, large-scale problems. This paper devotes to analyze the working mechanism of SRC and discusses accelerated l1-min techniques using augmented Lagrangian methods,consequently, we propose a very simple yet much more efficient face classification scheme. The performance of the new algorithms is demonstrated in a robust face recognition of AR database. The experimental results verify that these methods can greatly improve the face recognition speed rate（97% decrease）, and maintain a high recognition rate（95%）. These methods are of practical values.

关 键 词：稀疏编码 分类方法 人脸识别 小波变换 快速算法 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]