基于互补子空间线性判别分析的人脸识别

Linear Discriminant Analysis in Complementary Subspace for Face Recognition

基于随机子空间,提出了一种用于人脸识别的互补子空间线性判别分析方法.与Fisherface和零空间线性判别分析相比,该方法同时在主元子空间和零空间中进行判别分析,并在特征层融合这两个子空间的判别特征.根据最适宜的零空间状态构建随机子空间,随机子空间的融合在决策层进行.多个人脸数据库上的实验结果表明,本算法能够有效地解决线性判别分析中的小样本规模问题.

Based on random subspace, a complementary subspace linear discriminant analysis （LDA） approach is presented for face recognition. Compared with the Fisherface and the null space LDA which only perform the discriminant analysis in the principal and null subspaces respectively, the proposed method extracts discriminative information from the two subspaces simultaneously and combines the two parts discriminative features on the feature level. Furthermore, random subspace is generated under the most suitable situation for the null space and all random subspaces are integrated on the decision level. Experiments demonstrate that the proposed method can effectively solve the small sample size problem of LDA.