Low-resolution is an important issue when handling real world image recognition problems. The performance of traditional recognition algorithms, e.g. LDA/PCA, usually drops drastically due to the loss of discriminant information compared to those for high-resolution or super-resolution images. In order to solve this problem, many methods have been proposed in recent years based on coupled projections, i.e. learning two sets of different projections, one for high-resolution images and one for low-resolution images. For example, SDA (simultaneous discriminant analysis) obtains projections by maximizing the average between-class scatter while minimizing the average within-class scatters. Like LDA, SDA cannot separate projected classes, especially for those that are closer to each other. In this paper, a novel discriminant analysis method is proposed to achieve the optimal projections by maximizing the minimum distance between pair-wise classes. Experiments on several image datasets verify the efficiency of the presented methods.