Abstract
In this paper, we propose a method for pose-invariant facial expression recognition from monocular video sequences. The advantage of our method is that, unlike existing methods, our method uses a very simple model, called the variable-intensity template, for describing different facial expressions, making it possible to prepare a model for each person with very little time and effort. Variable-intensity templates describe how the intensity of multiple points defined in the vicinity of facial parts varies for different facial expressions. By using this model in the framework of a particle filter, our method is capable of estimating facial poses and expressions simultaneously. Experiments demonstrate the effectiveness of our method. A recognition rate of over 90% was achieved for horizontal facial orientations on a range of ±40 degrees from the frontal view.
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
Otsuka, K., Yamato, J., Takemae, Y., Murase, H.: Conversation scene analysis with Dynamic Bayesian Network based on visual head tracking. In: Proc. of the IEEE International Conference on Multimedia and Expo, pp. 949–952. IEEE Computer Society Press, Los Alamitos (2006)
Cohen, I., Sebe, N., Chen, L., Garg, A., Huang, T.: Facial expression recognition from video sequences: Temporal and static modeling. Computer Vision and Image Understanding 91, 160–187 (2003)
Kaliouby, R., Robinson, P.: Generalization of a vision-based computational model of mind-reading. In: Proc. of the First International Conference on Affective Computing and Intelligent Interatction, pp. 582–589 (2005)
Chang, Y., Hu, C., Feris, R., Turk, M.: Manifold based analysis of facial expression. Image and Vision Computing 24, 605–614 (2006)
Bartlett, M., Littlewort, G., Frank, M., Lainscsek, C., Fasel, I., Movellan, J.: Automatic recognition of facial actions in spontaneous expressions. Journal of Multimedia 1, 22–35 (2006)
Gokturk, S.B., Tomasi, C., Girod, B., Bouguet, J.Y.: Model-based face tracking for view-independent facial expression recognition. In: Proc. of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 287–293. IEEE Computer Society Press, Los Alamitos (2002)
Oka, K., Sato, Y.: Real-time modeling of face deformation for 3D head pose estimation. In: Proc. of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures, pp. 308–320. IEEE Computer Society Press, Los Alamitos (2005)
Dornaika, F., Davoine, F.: Simultaneous facial action tracking and expression recognition using a particle filter. In: Proc. of the Tenth IEEE International Conference on Computer Vision, vol. 2, pp. 1733–1738 (2005)
Zhu, Z., Ji, Q.: Robust real-time face pose and facial expression recovery. In: Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 681–688. IEEE Computer Society Press, Los Alamitos (2006)
Lucey, S., Matthews, I., Hu, C., Ambadar, Z., Torre, F., Cohn, J.: AAM derived face representations for robust facial action recognition. In: Proc. of the 7th International Conference on Automatic Face and Gesture Recognition, pp. 155–160 (2006)
Gross, R., Matthews, I., Baker, S.: Generic vs. person specific Active Appearance Models. Image and Vision Computing 23, 1080–1093 (2005)
Matsubara, Y., Shakunaga, T.: Sparse template matching and its application to real-time object tracking. IPSJ Transactions on Computer Vision and Image Media 46(9), 17–40 (2005)
Viola, P.A., Jones, M.J.: Rapid object detection using a boosted cascade of simple features. In: Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 511–518. IEEE Computer Society Press, Los Alamitos (2001)
Rich, E., Knight, K.: Artificial intelligence, pp. 537–583. McGraw-Hill Book Company, New York (1991)
Jepson, A.D., Fleet, D.J., El-Maraghi, T.F.: Robust online appearance models for visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1296–1311 (2003)
Belhumeur, P.N., Hespanha, J., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 711–720 (1997)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kumano, S., Otsuka, K., Yamato, J., Maeda, E., Sato, Y. (2007). Pose-Invariant Facial Expression Recognition Using Variable-Intensity Templates. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76386-4_30
Download citation
DOI: https://doi.org/10.1007/978-3-540-76386-4_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76385-7
Online ISBN: 978-3-540-76386-4
eBook Packages: Computer ScienceComputer Science (R0)