基于稀疏表示的分类方法SRC与基于协同表示的分类方法CRC分别通过L1范数和L2范数最小化获得具有稀疏性的线性表示系数,在人脸识别中取得了很好的效果.为了解决这两种方法没有考虑数据局部信息的问题,提出了基于局部表示的分类方法LRC.LRC使用测试样本局部范围内的训练样本对其进行线性表示,这样获得的局部表示系数在保持稀疏性的同时包含有效的局部信息.另外,通过求解一简单的约束最优化问题,LRC可快速获取局部表示系数.在ORL、YALE以及FERET人脸数据库上的实验结果,表明了LRC的有效性和高效性.%SRC (Sparse Representation based Classification) and CRC (Collaborative Representation based Classification) achieve linear representation coefficients with sparsity by L1 norm and L2 norm minimization respectively and perform very well in face recognition.However,SRC and CRC do not consider the local information of the data.To solve this problem,this paper proposes LRC (Local Representation based Classification).LRC uses training samples in the local scope of the test sample to represent it and obtains local representation coefficients.The local representation coefficients keep sparsity and contain effective local information.LRC can find the local representation coefficients quickly by solving a simple constrained optimization problem.Experimental results on ORL,YALE and FERET face databases demonstrate the effectiveness and efficiency of LRC.
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