Abstract
In this work, we examine landmark localization and feature extraction approaches for the unexplored topic of therapeutic facial exercise recognition. Our goal is to automatically discriminate nine therapeutic exercises that have been determined in cooperation with speech therapists. We use colour, 2.5D and 3D image data that was recorded using Microsoft’s Kinect. Our features comprise statistical descriptors of the face surface curvature as well as characteristic profiles that are derived from face landmarks. For the nine facial exercises, we yield an average recognition accuracy of about \(91\,\%\) in conjunction with manually labeled landmarks. Additionally, we introduce a combined method for automatic landmark localization and compare the results to landmark positions obtained from Active Appearance Model fitting as well as manual labeling. The combined localization method exhibits increased robustness in comparison to AAMs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lanz, C., Denzler, J., Gross, H.M.: Facial movement dysfunctions: conceptual design of a therapy-accompanying training system. In: Wichert, R., Klausing, H. (eds.) Ambient Assisted Living - Advanced Technologies and Societal Change. Springer, Heidelberg (2013)
Grosse, M., Schaffer, M., Harendt, B., Kowarschik, R.: Fast data acquisition for three-dimensional shape measurement using fixed-pattern projection and temporal coding. Opt. Eng. 50, 100503 (2011)
Cootes, T., Edwards, G., Taylor, C.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23, 681–685 (2001)
Nakamura, K., Toda, N., Sakamaki, K., Kashima, K., Takeda, N.: Biofeedback rehabilitation for prevention of synkinesis after facial palsy. Otolaryngol. Head Neck Surg. 128, 539–543 (2003)
Gebhard, A., Paulus, D., Suchy, B., Wolf, S.: A system for diagnosis support of patients with facialis paresis. Kl 3/2000, 40–42 (2000)
Colombo, A., Cusano, C., Schettini, R.: 3d face detection using curvature analysis. Pattern Recogn. 39, 444–455 (2006)
Wang, J., Yin, L., Wei, X., Sun, Y.: 3d facial expression recognition based on primitive surface feature distribution. In: International Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1399–1406 (2006)
Chua, C.S., Jarvis, R.: Point signature: a new representation for 3d object recognition. Int. J. Comput. Vis. 25, 63–85 (1997)
Chua, C.S., Han, F., Ho, Y.K.: 3d human face recognition using point signature. In: Proceedings of the 4th International Automatic Face and Gesture Recognition Conference, pp. 233–238 (2000)
Wang, Y., Chua, C.S., Ho, Y.K.: Facial feature detection and face recognition from 2d and 3d images. Pattern Recogn. Lett. 23, 1191–1202 (2002)
Besl, P., Jain, R.: Invariant surface characteristics for 3d object recognition in range images. Comput. Vis. Graph. Image Process. 33, 33–80 (1986)
Salomon, D.: Data Compression: The Complete Reference. Springer, New York (2004)
Martin, C., Werner, U., Gross, H.M.: A real-time facial expression recognition system based on active appearance models using gray images and edge images. In: International Conference on Automatic Face and Gesture Recognition (2008)
Viola, P., Jones, M.: Robust real-time face detection. Int. J. Comput. Vis. 57, 137–154 (2004)
Haase, D., Denzler, J.: Anatomical landmark tracking for the analysis of animal locomotion in x-ray videos using active appearance models. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 604–615. Springer, Heidelberg (2011)
Zhu, X., Ramanan, D.: Face detection, pose estimation and landmark localization in the wild. In: International Conference for Computer Vision and Pattern Recognition, pp. 2879–2886 (2012)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000)
Khoshelham, K.: Accuracy analysis of kinect depth data. In: ISPRS Workshop Laser Scanning, vol. 38 (2011)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). http://www.csie.ntu.edu.tw/~cjlin/libsvm
Hsu, C., Chang, C., Lin, C.: A practical guide to support vector classification. TR available at http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf (2009)
Webb, A., Copsey, K., Cawley, G.: Statistical Pattern Recognition. Wiley, New York (2011)
Acknowledgements
We would like to thank the m&i Fachklinik Bad Liebenstein (in particular Prof. Dr. med. Gustav Pfeiffer, Eva Schillikowski) and Logopädische Praxis Irina Stangenberger, who supported our work by giving valuable insights into rehabilitation and speech-language therapy requirements and praxis. This work is partially funded by the TMBWK ProExzellenz initiative, Graduate School on Image Processing and Image Interpretation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lanz, C., Olgay, B.S., Denzler, J., Gross, HM. (2014). Facial Landmark Localization and Feature Extraction for Therapeutic Face Exercise Classification. In: Battiato, S., Coquillart, S., Laramee, R., Kerren, A., Braz, J. (eds) Computer Vision, Imaging and Computer Graphics -- Theory and Applications. VISIGRAPP 2013. Communications in Computer and Information Science, vol 458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44911-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-662-44911-0_12
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44910-3
Online ISBN: 978-3-662-44911-0
eBook Packages: Computer ScienceComputer Science (R0)