This paper presents a discriminative temporal topic model (DTTM) for facial expression recognition. Our DTTM is developed by introducing temporal and categorical information into Latent Dirichlet Allocation (LDA) topic model. Temporal information is integrated by placing an asymmetric Dirichlet prior over document-topic distributions. The discriminative ability is improved by a supervised term weighting scheme. We describe the resulting DTTM in detail and show how it can be applied to facial expression recognition. Experiments on CMU expression database illustrate that the proposed DTTM is very effective in facial expression recognition. © 2011 Springer-Verlag.
CITATION STYLE
Shang, L., Chan, K. P., & Pan, G. (2011). DTTM: A discriminative temporal topic model for facial expression recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6938 LNCS, pp. 596–606). https://doi.org/10.1007/978-3-642-24028-7_55
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