快速局部协同表示分类器及其在人脸识别中的应用

Fast Local Collaborative Representation Based Classifier and Its Applications in Face Recognition

针对协同表示分类器(CRC)计算时间复杂度较高的问题,利用重构系数的大小与样本标签之间的正相关性,提出了局部快速协同表示器并用于人脸识别。首先使用最小二乘法求解L2范数约束下的线性回归问题;然后对重构系数进行筛选,舍弃对分类不利的负重构系数;最后抛弃原CRC算法中的样本重构环节,转而使用最大相似性准则确定测试样本所属分类。该方法利用样本的局部相似性,使识别率得到了一定的提升。同时该方法无需样本重构,求解复杂度大幅度降低。在AR和CMU PIE数据集上的实验结果表明,所提方法的时间复杂度极大幅度优于CRC,且在各种光照、表情、角度等状态下其识别率均高于现有其他相关算法。

To solve the problem of high computational time complexity of collaborative representation based classification method(CRC),this paper proposes a local fast collaborative representation based classifier for face recognition by using the positive correlation between the reconstruction coefficient and sample labels.Firstly,the least square method is used to solve the linear regression problem with a L2 norm constraint,and then the negative reconstruction coefficients which are unsuitable for classification are discarded.Finally,the maximum similarity criterion instead of the reconstruction criterion in CRC is adopted to determine the label of the test sample.The proposed method can receive better performance by taking local similarity into account,and consumes much less time without sample reconstruction than CRC.The experimental results on AR and CMU PIE datasets demonstrate that the proposed method consumes much less time than CRC,and can achieve better recognition accuracy than some state-of-the-art methods with varying illuminations,expressions and angles in facial images.