Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/84266
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Type: Conference paper
Title: The fusion of deep learning architectures and particle filtering applied to lip tracking
Author: Carneiro, G.
Nascimento, J.
Citation: Proceedings of 20th International Conference on Pattern Recognition (ICPR), 2010 / pp.2065-2068
Publisher: IEEE Computer society
Publisher Place: USA
Issue Date: 2010
ISBN: 9781424475421
ISSN: 1051-4651
Conference Name: International Conference on Pattern Recognition (20th : 2010 : Istanbul, Turkey)
Statement of
Responsibility: 
Gustavo Carneiro and Jacinto C. Nascimento
Abstract: This work introduces a new pattern recognition model for segmenting and tracking lip contours in video sequences. We formulate the problem as a general nonrigid object tracking method, where the computation of the expected segmentation is based on a filtering distribution. This is a difficult task because one has to compute the expected value using the whole parameter space of segmentation. As a result, we compute the expected segmentation using sequential Monte Carlo sampling methods, where the filtering distribution is approximated with a proposal distribution to be used for sampling. The key contribution of this paper is the formulation of this proposal distribution using a new observation model based on deep belief networks and a new transition model. The efficacy of the model is demonstrated in publicly available databases of video sequences of people talking and singing. Our method produces results comparable to state-of-the-art models, but showing potential to be more robust to imaging conditions.
Rights: © 2010 IEEE
RMID: 0020114361
DOI: 10.1109/ICPR.2010.508
Appears in Collections:Computer Science publications

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