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  • Research Article
  • Open Access

Comparison of Image Transform-Based Features for Visual Speech Recognition in Clean and Corrupted Videos

EURASIP Journal on Image and Video Processing20072008:810362

  • Received: 28 February 2007
  • Accepted: 17 December 2007
  • Published:


We present results of a study into the performance of a variety of different image transform-based feature types for speaker-independent visual speech recognition of isolated digits. This includes the first reported use of features extracted using a discrete curvelet transform. The study will show a comparison of some methods for selecting features of each feature type and show the relative benefits of both static and dynamic visual features. The performance of the features will be tested on both clean video data and also video data corrupted in a variety of ways to assess each feature type's robustness to potential real-world conditions. One of the test conditions involves a novel form of video corruption we call jitter which simulates camera and/or head movement during recording.


  • Image Processing
  • Pattern Recognition
  • Computer Vision
  • Feature Type
  • Head Movement

Publisher note

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Authors’ Affiliations

School of Electronics, Electrical Engineering and Computer Science, Queen's University of Belfast, Belfast, BT7 1NN, Northern Ireland, UK


© Rowan Seymour et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.