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Learning Sequential Patterns for Lipreading

Ong, E and Bowden, R Learning Sequential Patterns for Lipreading In: BMVC 2011, 2011-08-29 - 2011-09-02, Dundee, UK.

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This paper proposes a novel machine learning algorithm (SP-Boosting) to tackle the problem of lipreading by building visual sequence classifiers based on sequential patterns. We show that an exhaustive search of optimal sequential patterns is not possible due to the immense search space, and tackle this with a novel, efficient tree-search method with a set of pruning criteria. Crucially, the pruning strategies preserve our ability to locate the optimal sequential pattern. Additionally, the tree-based search method accounts for the training set’s boosting weight distribution. This temporal search method is then integrated into the boosting framework resulting in the SP-Boosting algorithm. We also propose a novel constrained set of strong classifiers that further improves recognition accuracy. The resulting learnt classifiers are applied to lipreading by performing multi-class recognition on the OuluVS database. Experimental results show that our method achieves state of the art recognition performane, using only a small set of sequential patterns.

Item Type: Conference or Workshop Item (Conference Poster)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors : Ong, E and Bowden, R
DOI : 10.5244/C.25.55
Contributors :
ContributionNameEmailORCID Press,
Additional Information : © The author(s). Published by The British Machine Vision Association (BMVA)
Depositing User : Symplectic Elements
Date Deposited : 25 May 2012 08:58
Last Modified : 06 Jul 2019 05:10

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