The minimum noise and correlation energy (MINACE) distortion-invariant filter (DIF) finds use in several applications such as face recognition, automatic target recognition (ATR), etc., in which one considers both true-class object classification and rejection of non-database objects (that are labeled as impostors in face recognition, and confusers in ATR). To solve the classification/rejection problem, we use at least one Minace filter per object class to be recognized. A separate Minace filter or a set of Minace filters is synthesized for each object class. The Minace parameter c trades-off distortion-tolerance (recognition) versus discrimination (impostor/confuser rejection) performance. We present our automated Minace filter-synthesis algorithm (auto-Minace) that selects the training set images to be included in the filter and selects the filter parameter c, so that the filter can achieve both good recognition and impostor/confuser and clutter rejection performance; this is achieved using a training and validation set. No impostor/confuser, clutter or test set data is present in the training or validation sets. The peak-to-correlation energy (PCE) ratio is used as the correlation plane metric in both filter synthesis and in tests, since it gives better results than use of the correlation peak value. We also address the use of the Minace filters in detection applications where the filter template is much smaller than the target scene. The use of circular versus linear correlations are addressed, circular correlations require less storage and fewer online computations.