Principal Components Analysis (PCA）and Linear Discriminant Analysis (LDA）are two popular feature extraction methods for pattern recognition

and in image recognition

researchers usually use PCA+LDA instead of LDA.An enhanced linear discriminant analysis (ELDA）criterion

which integrates their merits

is proposed in the paper.It can not only overcome the PCA’s shortcomings of lower precision when using the minimal distance

but also resolve the problem of projective vector solution of LDA when the within class scatter matrix is singular.So the two step method of PCA+LDA can be substituted by ELDA.Moreover

its recognition rate exceeds the single PCA

LDA

or PCA+LDA largely.Many experiments on ORL

YALE and NUST603 face database indicate that our method is effective.