基于两空间线性鉴别分析的小样本人脸识别

指出了线性鉴别分析及其几种改进方法在处理小样本人脸识别问题时存在的不足,提出了一种基于两空间线性鉴别分析的小样本人脸识别方法.首先将样本投影到总体散布矩阵的非零空间中进行分析;进而将类内散布矩阵分成零空间和非零空间进行鉴别向量确定和鉴别特征提取,最后将得到的两种鉴别特征融合,从而使用最近邻法进行分类.实验结果表明,在进行小样本的人脸识别时,该方法的识别效果优于其他线性方法.

Face Recognition Based on Two Subspaces Linear Discriminant Analysis

Disadvantages of the several typical variants on Linear Discriminant Analysis (LDA) while dealing with the well-known small sample size problem in face recognition are revealed. A new discriminant analysis method named two subspaces LDA is proposed to deal with small sample size problem. Firstly, all the samples are projected to the nonzero space of the total scatter matrix. Secondly, discriminant analysis is carried out in the zero subspace and nonzero subspace of the within-class scatter matrix respective...