The diagnosis of disease with the aid of computer programs has been developing more and more in recent years. This paper presents an approach which is based on frequency technique for the objective quantitative analysis of facial paralysis. In this method, limited-orientation modified circular Gabor filters (LO-MCGFs) are used to enhance the desirable frequencies in images. Then, features are extracted from the filtered images for classification. The first advantage of the LOMCGF is that its inner passbands are uniform, so it helps remove noise and control frequencies more effectively. The second benefit is that the LO-MCGF utilizes the existing robust characteristics of circular Gabor filter for rotation invariant texture regions. Hence, the LO-MCGF-based technique improves remarkably the accuracies of score estimation for some expressions whose local textures are invariant in rotation. Finally, the limited filtered regions, or limited propagation orientations, help the LO-MCGF focus on only some specific spaces. Therefore, the LO-MCGF can avoid the influences of irrelevant regions. In other words, it improves the spatial localization. For overall evaluation, experiments show that our proposed method is superior to other contemporary techniques tested on a dynamic facial expression database.