一种改进的线性判别分析算法在人脸识别中的应用

摘要

Linear discriminant analysis (LDA) is a typical feature extraction method, but there exist at least two critical drawbacks in LDA: the small sample size problem and the rank limitation problem. In order to solve the above problems, this paper presents an improved LDA method (ILDA) which redefines the between-class scatter matrix and the within-class scatter matrix. ILDA can effectively extract the discriminative information included in the null subspace and the non-null subspace of a within-class scatter matrix. Numerical experiments on some facial databases show ILDA achieves good performance of face recognition.%线性判别分析算法是一种经典的特征提取方法,但其仅在大样本情况下适用.本文针对传统线性判别分析算法面临的小样本问题和秩限制问题,提出了一种改进的线性判别分析算法ILDA.该方法在矩阵指数的基础上,重新定义了类内离散度矩阵和类间离散度矩阵,有效地同时提取类内离散度矩阵零空间和非零空间中的信息.若干人脸数据库上的比较实验表明了ILDA在人脸识别方面的有效性.