Abstract
In this paper we extend the latent Dirichlet allocation (LDA) topic model to model facial expression dynamics. Our topic model integrates the temporal information of image sequences through redefining topic generation probability without involving new latent variables or increasing inference difficulties. A collapsed Gibbs sampler is derived for batch learning with labeled training dataset and an efficient learning method for testing data is also discussed. We describe the resulting temporal latent topic model (TLTM) in detail and show how it can be applied to facial expression recognition. Experiments on CMU expression database illustrate that the proposed TLTM is very efficient in facial expression recognition.
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
Bassili, J.: Emotion recognition: the role of facial movement and the relative importance of upper and lower areas of the face. Personality and Social Psychology, 2049–2059 (1979)
Blei, D., Ng, A., Jordan, M.: Latent dirichlet allocation. JMLR 3(2-3), 993–1022 (2003)
Canini, K.R., Shi, L., Griffiths, T.L.: Online inference of topics with latent Dirichlet allocation. In: AISTATS (2009)
Chang, Y., Hu, C., Turk, M.: Probabilistic expression analysis on manifolds. In: CVPR, pp. 520–527 (2004)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. on PAMI 23(6), 681–685 (2001)
Ekman, P., Friesen, W.V.: Facial Action Coding System (FACS): Manual. Consulting Psychologists Press, Palo Alto (1978)
Griffiths, T.L., Steyvers, M.: Finding scientific topics. PNAS 101, 5228–5235 (2004)
Hanna, M.W.: Topic modeling: beyond bag-of-words. In: ICML (2006)
Hospedales, T., Gong, S., Xiang, T.: A Markov clustering topic model for mining behaviour in video. In: ICCV (2009)
Jin, N., Mokhtarian, F.: A non-parametric HMM learning method for shape dynamics with application to human motion recognition. In: ICPR, pp. 29–32 (2006)
Kanade, T., Cohn, J.F., Tian, Y.: Comprehensive database for facial expression analysis. In: FG, pp. 46–53 (2000)
Kumano, S., Otsuka, K., Yamato, J., Maeda, E., Sato, Y.: Pose-invariant facial expression recognition using variable-intensity templates. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part I. LNCS, vol. 4843, pp. 324–334. Springer, Heidelberg (2007)
Lacoste-Julien, S., Sha, F., Jordan, M.I.: DiscLDA: Discriminative learning for dimensionality reduction and classification. In: NIPS, pp. 897–904 (2008)
Lefevre, F.: Nonparametric probability estimation for HMM-based automatic speech recognition. Computer Speech and Language 17(2-3), 113–136 (2003)
Fei-Fei, L., Perona, P.: A bayesian hierarchical model for learning natural scene categories. In: CVPR, pp. 524–531 (2005)
Minka, T., Lafferty, J.: Expectation propagation for the generative aspect model. In: UAI, pp. 352–359 (2002)
Shan, C., Gong, S., McOwan, P.W.: Robust facial expression recognition using local binary patterns. In: ICIP, pp. 370–373 (2005)
Shang, L., Chan, K.P.: Temporal Exemplar-Based Bayesian Networks for Facial Expression Recognition. In: ICMLA, pp. 16–22 (2008)
Shang, L., Chan, K.P.: Nonparametric Discriminant HMM and Application to Facial Expression Recognition. In: CVPR, pp. 2090–2096 (2009)
Steyvers, M., Smyth, P., Rosen-Zvi, M., Griffiths, T.: Probabilistic Author-Topic Models for Information Discovery. In: KDD, pp. 306–315 (2004)
Teh, Y.W., Jordan, M.I., Beal, M.J., Blei, D.: Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes. In: NIPS (2004)
Tian, Y., Kanade, T., Cohn, J.: Recognizing action units for facial expression analysis. IEEE Trans. on PAMI 23(2), 97–115 (2001)
Wang, X., Grimson, E.: Spatial latent dirichlet allocation. In: NIPS (2007)
Yang, P., Liu, Q., Metaxas, D.N.: Boosting coded dynamic features for facial action units and facial expression recognition. In: CVPR, pp. 1–6 (2007)
Yeasin, M., Bullot, B., Sharma, R.: From facial expression to level of interest: a spatio-temporal approach. In: CVPR, pp. 922–927 (2004)
Zhang, Y., Ji, Q.: Active and dynamic information fusion for facial expression understanding from image sequences. IEEE Trans. on PAMI 27(5), 699–714 (2005)
Zhao, G., Pietikäinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. on PAMI 29(6), 915–928 (2007)
Zhu, J., Ahmed, A., Xing, E.P.: MedLDA: Maximum margin supervised topic models for regression and classification. In: ICML (2009)
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
Shang, L., Chan, KP. (2011). A Temporal Latent Topic Model for Facial Expression Recognition. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19282-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-19282-1_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19281-4
Online ISBN: 978-3-642-19282-1
eBook Packages: Computer ScienceComputer Science (R0)