In a wide range of color-related computer vision applications, researchers tried to select one of the conventional color spaces as the optimum one. This paper, however, addresses the problem of how to learn an optimum color space from the given training sample set. We seek a set of optimal coefficients to combine the R, G and B components based on a discriminant criterion and then gain one discriminant color component for representing color image for recognition purposes. Further, we can obtain three sets of optimal combination coefficients and use them to generate a three-dimensional discriminant color space (DCS). The proposed DCS method was assessed on Experiment 4 of the Face Recognition Grand Challenge (FRGC) database and the experimental results show the proposed discriminant color space significantly outperforms the RGB and Ig(r-g) color spaces.