中文摘要：

现实中，常需辨识低分辨率（low-resolution，简称LR）图像（如监控系统所捕捉的人脸），但相比通常的高（high-resolution，简称HR）或超（super-resolution，简称SR）分辨率图像而言，其含有相对较少的判别信息，致使通常的子空间学习算法，如结合主成分分析（principal components analysis，简称PCA）的线性判别分析（1inear discriminant analysis，简称LDA）难以获得理想的识别效果．为了缓和该问题，最近所提出的联合判别分析（如SDA）借助与低分辨率相配对的高分辨率图像辅助设计LR图像分类器．在SDA的实现中，其采用了类似LDA的平均散度定义，使SDA遗传了LDA在投影时难以使相对靠近的类充分分离的问题．为了克服该不足，提出了针对LR图像识别的最坏分离的联合分辨率判别分析（worst—separated couple-resolution discriminant analysis，简称WSCR），从而使：（1）LR和HR投影到同一低维子空间；（2）投影后的最小类间隔最大化．实验结果表明：与SDA相比，WSCR更适用于低分辨率的图像识别．

英文摘要：

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.