Authors:
Cornelia Lanz
1
;
Birant Sibel Olgay
1
;
Joachim Denzler
2
and
Horst-Michael Gross
1
Affiliations:
1
Ilmenau University of Technology, Germany
;
2
Friedrich Schiller University Jena, Germany
Keyword(s):
Facial Expressions, Curvature Analysis, Point Signatures, Line Profiles, Therapeutic Exercises.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Medical Image Applications
Abstract:
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 benefi
cial for a real-world, fully automated scenario as well.
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