Objective evaluation of disease is one of the desirable goals in medicine. This paper presents a technique for the objective evaluation of facial paralysis, in which features are extracted based on landmark's positions in three-dimensional space (3D-landmarks). The landmarks are initialized manually in the first frontal frame and are tracked in the subsequent frontal frames. Then, the landmark's positions are reconstructed in 3D-space using multiview images and a camera self-calibration technique. From the 3D-landmarks, the features are extracted in 3D-space (called 3D-features) and used for classification. These 3D-features may contain enhanced information such as depth information and, therefore, may help improve the accuracy rates of predicted scores. In addition, our method uses the camera self-calibration technique for estimating the camera's parameters, and does not use laser scanning for 3D reconstruction, so it is more flexible to set up and safer for the patient. For overall evaluation, experiments showed that our technique achieved superior results to other methods.