This paper presents a method for improving the performance of matching systems that correlate using shape templates. The basic idea involves extending an existing set of training shapes with generated "virtual" shapes, in order to improve representational capability. Yet no a-priori feature correspondence is necessary among the original shapes in the training set. Instead, an integrated clustering and registration approach partitions the original shape samples into clusters of similar and registered shapes; in each cluster a separate feature space is embedded. This allows for each cluster the derivation of standard compact parameterizations. This paper demonstrates that sampling these low-order spaces can produce an extended training set which facilitates a superior matching performance, as measured by a ROC curve. In the experiments, we consider a realistic application involving thousands of pedestrian shapes and perform correlation matching based on distance transforms.