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

  • Wai Chee Yau
  • Dinesh Kant Kumar
  • Tharangini Chinnadurai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)

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.

Keywords

visual speech recognition motion segmentation feature selection Zernike moments support vector machines 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Wai Chee Yau
    • 1
  • Dinesh Kant Kumar
    • 1
  • Tharangini Chinnadurai
    • 1
  1. 1.School of Electrical and Computer EngineeringRMIT UniversityAustralia

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