特征空间中的拓展稀疏人脸识别

基于稀疏表示分类（SRC，sparse representation for classification）是近年来模式识别领域中备受关注的一个研究热点。当每类训练样本较少时，SRC的识别效果往往不理想。为解决此问题，人们提出了拓展的稀疏表示分类算法。它引入了训练样本的类内变量矩阵，来补充每类训练样本信息。但是，该方法很难获取普遍存在于复杂数据如图像中的非线性信息。为此，提出了特征空间中的拓展稀疏人脸识别算法。该算法将样本集非线性映射到新的特征空间中，计算每个训练样本在表示测试样本时所做的贡献。根据贡献大小，给每个训练样本赋予一定的权重。同时，利用类内变量矩阵，共同表示测试样本。实验表明所提出的算法优于其它经典稀疏表示分类算法。

Extended sparse representation for face recognition in feature space

Sparse representation for classification (SRC) has attracted much attention in the field of pattern recognition in recent years. If each class has few training samples, SRC usually cannot achieve the desirable recognition performance. To address the above problem, extended sparse representation for classification (ESRC) is proposed,which uses the intraclass variant matrix to supplement the training sample information. Nevertheless, ESRC can hardly capture the nonlinear information in complex data such as images. An extended sparse representation in a feature space for classification algorithm was proposed, in which the original data were mapped into a new high dimensional space through a nonlinear mapping to evaluate the contribution of each training sample in the representation of test sample, and each sample was given a certain weight according to the contribution. Then, the proposed algorithm used the weighted training samples combining the intraclass variant matrix to represent the test samples. Experiments show that the proposed algorithm is superior to other typical sparse representation for classification algorithms.