In the traditional DKL algorithm, PCA is performed before LDA to achieve stable numerical computation and good generalization in small-sample-size problems. However, PCA is based on the global information, ignoring the significant local characteristics. This paper will propose a novel algorithm called discriminant local feature analysis based on a broader understanding of LFA features. In the algorithm, LFA instead of PCA is applied before LDA. On the one hand, LFA captures local characteristics with little loss of global information. On the other hand, it presents an effective low-dimensional representation of signals, and thus reduces the dimensionality for LDA. By combining LFA and LDA, the DLFA algorithm outperforms DKL, which is showed by experiments on open-set face verification.