基于低秩表示与矩阵填充的人脸识别方法

Low-Rank Representation and Matrix Completion Based Face Recognition

基于回归分析的人脸识别方法在处理不完备数据矩阵时,先对矩阵进行填充,再使用人脸识别方法,因此会降低分类性能.为了更有效地执行关于不完备数据的识别,文中将低秩矩阵填充和低秩表示学习整合在同一个模型,提出基于低秩表示和低秩矩阵填充的人脸识别方法.通过最小化表示系数和矩阵秩交替计算样本低秩表示系数矩阵和恢复矩阵缺失项,再使用最近邻分类器实现分类.在一些公开人脸数据集上的实验表明,在训练样本矩阵元素随机缺失时,文中方法可以有效提高识别精度及降低填充误差.

When the face recognition method based on regression analysis is applied to the incomplete matrix, it completes the matrix firstly before using the face recognition method. Thus, the classification performance is reduced. To solve the problem, a face recognition method based on low-rank representation and low-rank matrix completion is proposed by integrating low-rank matrix completion and low-rank representation learning into a single model. The low-rank representation coefficient matrix is computed alternately and the missing entries are recovered by minimizing the representation coefficients and matrix rank. Then, the nearest neighbor classifier is used to classify the samples. Experimental results on several open face datasets show that the proposed method effectively improves the recognition performance and reduces the error of matrix completion while the entries of the training sample matrix are randomly missing.