Lip-Reading Technique Using Spatio-Temporal Templates and Support Vector Machines

Authors:
Wai Chee Yau;Dinesh Kant Kumar;Tharangini Chinnadurai
Affiliations:
School of Electrical and Computer Engineering, RMIT University,;School of Electrical and Computer Engineering, RMIT University,;School of Electrical and Computer Engineering, RMIT University,
Venue:
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Year:
2008

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Abstract

This paper presents a lip-reading technique to identify the unspoken phones using support vector machines. The proposed system is based on temporal integration of the video data to generate spatio-temporal templates (STT). 64 Zernike moments (ZM) are extracted from each STT. This work proposes a novel feature selection algorithm to reduce the dimensionality of the 64 ZM to 12 features. The proposed technique uses the shape of probability curve as a goodness measure for optimal feature selection. The feature vectors are classified using non-linear support vector machines.Such a system could be invaluable when it is important to communicate without making a sound, such as giving passwords when in public spaces.