Abstract
The modelling and understanding of the facial dynamics of individuals is crucial to achieving higher levels of realistic facial animation. We address the recognition of individuals through modelling the facial motions of several subjects. Modelling facial motion comes with numerous challenges including accurate and robust tracking of facial movement, high dimensional data processing and non-linear spatial-temporal structural motion. We present a novel framework which addresses these problems through the use of video-specific Active Appearance Models (AAM) and Gaussian Process Latent Variable Models (GP-LVM). Our experiments and results qualitatively and quantitatively demonstrate the framework’s ability to successfully differentiate individuals by temporally modelling appearance invariant facial motion. Thus supporting the proposition that a facial activity model may assist in the areas of motion retargeting, motion synthesis and experimental psychology.
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
Tang, H., Fu, Y., Tu, J., Huang, T.S., Hasegawa-Johnson, M.: EAVA: A 3D Emotive Audio-Visual Avatar. In: WACV 2008, pp. 1–6 (2008)
Kähler, K., Haber, J., Seidel, H.P.: Reanimating the Dead: Reconstruction of Expressive Faces from Skull Data. In: SIGGRAPH 2003, pp. 554–561 (2003)
MacDorman, K.F., Green, R.D., Ho, C.C., Koch, C.T.: Too Real for Comfort? Uncanny Responses to Computer Generated Faces. Computers in Human Behavior 25, 695–710 (2009)
Ye, N., Sim, T.: Combining Facial Appearance and Dynamics for Face Recognition. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 133–140. Springer, Heidelberg (2009)
Yang, P., Liu, Q., Metaxas, D.: Dynamic Soft Encoded Patterns for Facial Event Analysis. Computer Vision and Image Understanding 115, 456–465 (2011)
Hadid, A., Pietikäinen, M., Li, S.Z.: Learning personal specific facial dynamics for face recognition from videos. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 1–15. Springer, Heidelberg (2007)
Fan, X., Sun, Y., Yin, B., Guo, X.: Gabor-based Dynamic Representation for Human Fatigue Monitoring in Facial Image Sequences. Pattern Recognition Letters 31, 234–243 (2010)
Raducanu, B., Dornaika, F.: Dynamic vs. Static recognition of facial expressions. In: Aarts, E., Crowley, J.L., de Ruyter, B., Gerhäuser, H., Pflaum, A., Schmidt, J., Wichert, R. (eds.) AmI 2008. LNCS, vol. 5355, pp. 13–25. Springer, Heidelberg (2008)
Dornaika, F., Lazkano, E., Sierra, B.: Improving Dynamic Facial Expression Recognition with Feature Subset Selection. Pattern Recognition Letters 32, 740–748 (2011)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)
Asthana, A., Saragih, J., Wagner, M., Goecke, R.: Evaluating AAM Fitting Methods for Facial Expression Recognition. In: ACII 2009, pp. 1–8 (2009)
Matthews, I., Baker, S.: Active Appearance Models Revisited. International Journal of Computer Vision 60, 135–164 (2004)
Xiao, J., Baker, S., Matthews, I., Kanade, T.: Real-time Combined 2D+3D Active Appearance Models. In: CVPR 2004, vol. 2, pp. 535–542 (2004)
Lawrence, N., Hyvärinen, A.: Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models. Journal of Machine Learning Research 6, 1783–1816 (2005)
Grochow, K., Martin, S.L., Hertzmann, A., Popović, Z.: Style-based Inverse Kinematics. In: SIGGRAPH 2004, pp. 522–531 (2004)
Lawrence, N.D., Quiñonero Candela, J.: Local Distance Preservation in the GP-LVM through Back Constraints. In: ICML 2006, pp. 513–520 (2006)
Ek, C.H., Torr, P., Lawrence, N.D.: Gaussian Process Latent Variable Models for Human Pose Estimation. In: Popescu-Belis, A., Renals, S., Bourlard, H. (eds.) MLMI 2007. LNCS, vol. 4892, pp. 132–143. Springer, Heidelberg (2008)
Quirion, S., Duchesne, C., Laurendeau, D., Marchand, M.: Comparing GPLVM Approaches for Dimensionality Reduction in Character Animation. WSCG 16, 41–48 (2008)
Huang, M., Wang, Z., Ying, Z.: A Novel Method of Facial Expression Recognition Based on GPLVM Plus SVM. In: ICSP 2010, pp. 916–919 (2010)
Deena, S., Galata, A.: Speech-driven facial animation using a shared gaussian process latent variable model. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Kuno, Y., Wang, J., Wang, J.-X., Wang, J., Pajarola, R., Lindstrom, P., Hinkenjann, A., Encarnação, M.L., Silva, C.T., Coming, D. (eds.) ISVC 2009. LNCS, vol. 5875, pp. 89–100. Springer, Heidelberg (2009)
Kanade, T., Cohn, J.F., Tian, Y.: Comprehensive Database for Facial Expression Analysis. In: AFGR 2000, pp. 46–53 (2000)
Wallhoff, F.: Facial Expressions and Emotion Database (2006)
Ekman, P., Friesen, W.V., Hager, J.C.: Facial Action Coding System. [CD-ROM] (2002)
Ramer, U.: An Iterative Procedure for the Polygonal Approximation of Plane Curves. Computer Graphics and Image Processing 1, 244–256 (1972)
Zhu, Z., Ji, Q.: Real Time 3D Face Pose Tracking From an Uncalibrated Camera. In: CVPR 2004, vol. 73 (2004)
Akhter, I., Sheikh, Y.A., Khan, S., Kanade, T.: Nonrigid Structure from Motion in Trajectory Space. Neural Information Processing Systems (2008)
Arthur, D., Vassilvitskii, S.: K-Means++: The Advantages of Careful Seeding. In: SODA 2007, pp. 1027–1035 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Davies, A., Ek, C.H., Dalton, C., Campbell, N. (2011). Facial Movement Based Recognition. In: Gagalowicz, A., Philips, W. (eds) Computer Vision/Computer Graphics Collaboration Techniques. MIRAGE 2011. Lecture Notes in Computer Science, vol 6930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24136-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-24136-9_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24135-2
Online ISBN: 978-3-642-24136-9
eBook Packages: Computer ScienceComputer Science (R0)