Abstract
Non-verbal communication signals are to a large part conveyed by visual motion information of the user’s facial components (intrinsic motion) and head (extrinsic motion). An observer perceives the visual flow as a superposition of both types of motions. However, when visual signals are used for training of classifiers for non-articulated communication signals, a decomposition is advantageous. We propose a biologically inspired model that builds upon the known functional organization of cortical motion processing at low and intermediate stages to decompose the composite motion signal. The approach extends previous models to incorporate mechanisms that represent motion gradients and direction sensitivity. The neural models operate on larger spatial scales to capture properties in flow patterns elicited by turning head movements. Center-surround mechanisms build contrast-sensitive cells and detect local facial motion. The model is probed with video samples and outputs occurrences and magnitudes of extrinsic and intrinsic motion patterns.
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
Preview
Unable to display preview. Download preview PDF.
References
Akakin, H.C., Sankur, B.: Robust Classification of Face and Head Gestures in Video. Image and Vision Computing 29, 470–483 (2011)
Argyle, M.: Bodily Communication, vol. 2. Routledge, London (1988)
Bascle, B., Blake, A.: Separability of Pose and Expression in Facial Tracking and Animation. In: Proc. Int. Conf. on Computer Vision, pp. 323–328 (1998)
Benyon, D.: Designing Interactive Systems, 2nd edn. Pearson, Addison–Wesley, London (2010)
Benyon, D., Mival, O.: Landscaping Personification Technologies: From Interactions to Relationships. In: CHI Ext. Abs. on Human Factors in Computing Systems, pp. 3657–3662 (2008)
Black, M., Yacoob, Y.: Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion. International Journal of Computer Vision 25(1), 23–48 (1997)
Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High Accuracy Optical Flow Estimation Based on a Theory for Warping. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004)
Donato, G., Bartlett, M.S., Hager, J.C., Ekman, P., Sejnowski, T.J.: Classifying Facial Actions. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(10), 974–989 (1999)
Frith, C.D., Wolpert, D.M.: The Neuroscience of Social Interaction: Decoding, imitating, and influencing the actions of others. Oxford University Press (2004)
Longuet-Higgins, H.C.: A Computer Algorithm for Reconstructing a Scene from Two Projections. Nature 293, 133–135 (1981)
Niese, R., Al-Hamadi, A., Farag, A., Neumann, H., Michaelis, B.: Facial Expression Recognition Based on Geometric and Optical Flow Features in Colour Image Sequences. IET Computer Vision, 1–11 (2011)
Orban, G.: Higher Order Visual Processing in Macaque Extrastriate Cortex. Physiology Review 88(88), 59–89 (2008)
Rosenblum, M., Yacoob, Y., Davis, L.S.: Human Expression Recognition from Motion Using a Radial Basis Function Network Architecture. IEEE Transactions on Neural Networks 7(5), 1121–1138 (1996)
Wendemuth, A., Biundo, S.: A Companion Technology for Cognitive Technical Systems. In: Esposito, A., Esposito, A.M., Vinciarelli, A., Hoffmann, R., Müller, V.C. (eds.) COST 2102. LNCS, vol. 7403, pp. 89–103. Springer, Heidelberg (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tschechne, S., Layher, G., Neumann, H. (2013). A Biologically Inspired Model for the Detection of External and Internal Head Motions. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-40728-4_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40727-7
Online ISBN: 978-3-642-40728-4
eBook Packages: Computer ScienceComputer Science (R0)