一种快速的基于稀疏表示的人脸识别算法

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%。

A Fast Face Recognition Algorithm Based on Sparse Representation

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.