摘要
提出了一种基于LBP算子和鲁棒稀疏表示的人脸识别方法。首先,提取训练样本和测试样本的LBP特征。其次,在原有稀疏表示分类器(SRC)的基础上添加一个权值矩阵W来解决l1正则化最小二乘问题。最后,利用鲁棒稀疏表示分类器(RSRC)分类测试人脸图像所属类别。在AT&T人脸库上进行实验的结果表明,此方法是优于其他经典算法的。
This paper proposes a new face recognition method based on LBP operator and RSR （Robust Sparse Representation）. Firstly, the LBP features of the training data and testing data are extracted. Secondly, on the basis of SRC, a weighted matrix W is added to solve a ll-regularized least square problem. Finally, RSRC is used to judge which class the face images belong to. Experimental results on AT＆T database demonstrate the new method has very good performance and superior over other classical methods.
出处
《电子技术（上海）》
2014年第6期1-3,共3页
Electronic Technology
