Facial paralysis is a common disease occurring with annual patient rate of 25 to 35/100 000. The symptom of the disease is that the patients loose or decrease facial movement ability. It is useful if there is an effective method to objective evaluation of facial paralysis degree. This paper presents a method to develop this tool based on filtered images. In our work, we propose a filter to extract useful isotropic frequencies of images in local spaces and remove unnecessary frequency components before feature extraction. The filter function is the modulation of an isotropic Gaussian function by a radial sinusoidal function. The interesting characteristic of this filter is that the passbands are the same for all orientations. This may be useful in some cases such as quantifying the degree of facial palsy. In this work, the measurement of symmetry and asymmetry between two sides of the face is performed on filtered images, and then the measured information is used for classification. Experiments have shown that with the use of our filtered technique, it gives superior results than the other methods testing on an available database of Osaka Police Hospital.