Skip to main content

Advertisement

We’d like to understand how you use our websites in order to improve them. Register your interest.

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

Abstract

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.

Publisher note

To access the full article, please see PDF.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Darryl Stewart.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Seymour, R., Stewart, D. & Ming, J. Comparison of Image Transform-Based Features for Visual Speech Recognition in Clean and Corrupted Videos. J Image Video Proc 2008, 810362 (2007). https://doi.org/10.1155/2008/810362

Download citation

Keywords

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