In this paper, a novel feature extraction method for palmprint recognition termed as Two-dimensional Combined
Discriminant Analysis (2DCDA) is proposed. By connecting the adjacent rows of a image sequentially, the obtained new
covariance matrices contain the useful information among local geometry structures in the image, which is eliminated by
2DLDA. In this way, 2DCDA combines LDA and 2DLDA for a promising recognition accuracy, but the number of
coefficients of its projection matrix is lower than that of other two-dimensional methods. Experimental results on the
CASIA palmprint database demonstrate the effectiveness of the proposed method.