We propose using a feature extraction scheme, Discriminant Component Analysis, for face recognition. This scheme decomposes a signal into orthogonal bases such that for each base there is an eigenvalue representing the discriminatory power of projection in that direction. The bases and eigenvalues are obtained by iteratively applying Fisher's Linear Discriminant Analysis (LDA). We illustrate the motivation of this scheme and show how it can be used to construct new distance metrics for the purpose of enhanced classification. Finally, good performance for face recognition on a dataset of 738 gallery images and 115 probe images is obtained using new distance metrics.