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Automated Classification of Therapeutic Face Exercises using the Kinect

In this work, we propose an approach for the unexplored topic of therapeutic facial exercise recognition using depth images. In cooperation with speech therapists, we determined nine exercises that are beneficial for therapy of patients suffering from dysfunction of facial movements. Our approach employs 2.5D images and 3D point clouds, which were recorded using Microsoft’s Kinect. Extracted features comprise the curvature of the face surface and characteristic profiles that are derived using distinctive landmarks. We evaluate the discriminative power and the robustness of the features with respect to the above-mentioned application scenario. Using manually located face regions for feature extraction, we achieve an average recognition accuracy of about 91% for the nine facial exercises. However in a real-world scenario manual localization of regions for feature extraction is not feasible. Therefore, we additionally examine the robustness of the features and show, that they are beneficial for a real-world, fully automated scenario as well.