Abstract: The principal component analysis (PCA) faces the problem of high computation complexity and inaccurate estimated covariance matrix from training face images for face recognition. The expressive feature face recognition algorithm (EFFRA) is proposed. In EFFRA, the subspace basic vector extracted by PCA is substituted by the right singular vectors of training images, so that the transformation from the images to image vectors is avoided. Hence the computation is simplified significantly. Further analysis shows that each principal component vector extracted by EFFRA still contains redundancy. Based on this result, a modified EFFRA (MEFFRA) is presented by combining the EFFRA and PCA. The results based on ORL and Essex database show that both EFFRA and MEFFRA are superior to eigenfaces, recognition ability of MEFFRA is no less than EFFRA with a much smaller storage space compared with EFFRA.