Paper Info
Title
A temporal latent topic model for facial expression recognition
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.
Year
DOI
Venue
2010
10.1007/978-3-642-19282-1_5
ACCV (4)
Keywords
Field
DocType
cmu expression database,new latent variable,topic model,proposed tltm,latent dirichlet allocation,topic generation probability,efficient learning method,facial expression recognition,temporal latent topic model,facial expression dynamic,gibbs sampler,latent variable,facial expression
Dynamic topic model,Latent Dirichlet allocation,Pattern recognition,Computer science,Inference,Latent variable,Facial expression,Probabilistic latent semantic analysis,Artificial intelligence,Topic model,Machine learning,Gibbs sampling
Conference
Volume
ISSN
Citations 
6495
0302-9743
3
PageRank 
References 
Authors
0.42
23
2
Name
Order
Citations
PageRank
Lifeng Shang148530.96
Kwok Ping Chan231323.52