关键词: 主成分分析, 线性判别分析, 高维小样本问题, 正交判别空间, 人脸识别

Abstract: In order to improve the orthogonality of discriminant subspace in Fisherface and increase the recognition rate, a new algorithm called matrix symmetry of Fisherface is proposed. The PCA is used for dimensionality reduction to eliminate the small sample size problem. The Fisher criterion is redefined by introducing a symmetric matrix. Some examples are classified by the symmetric matrix. Experimental results on ORL face image database indicate that the proposed method is more effective than the previous ones. And this method is more stable and less affected by the training set.

Key words: principal component analysis, linear discriminant analysis, high-dimensional and small sample size problem, orthogonal discriminant space, face recognition