Improving the Recognition Performance of Lip Reading Using the Concatenated Three Sequence Keyframe Image Technique

  • L. Poomhiran Faculty of Information Technology and Digital Innovation, King Mongkut’s University of Technology North Bangkok, Thailand https://orcid.org/0000-0003-2658-7973
  • P. Meesad Faculty of Information Technology and Digital Innovation, King Mongkut’s University of Technology North Bangkok, Thailand
  • S. Nuanmeesri Faculty of Science and Technology, Suan Sunandha Rajabhat University, Thailand https://orcid.org/0000-0002-2511-9820

Abstract

This paper proposes a lip reading method based on convolutional neural networks applied to Concatenated Three Sequence Keyframe Image (C3-SKI), consisting of (a) the Start-Lip Image (SLI), (b) the Middle-Lip Image (MLI), and (c) the End-Lip Image (ELI) which is the end of the pronunciation of that syllable. The lip area’s image dimensions were reduced to 32×32 pixels per image frame and three keyframes concatenate together were used to represent one syllable with a dimension of 96×32 pixels for visual speech recognition. Every three concatenated keyframes representing any syllable are selected based on the relative maximum and relative minimum related to the open lip’s width and height. The evaluation results of the model’s effectiveness, showed accuracy, validation accuracy, loss, and validation loss values at 95.06%, 86.03%, 4.61%, and 9.04% respectively, for the THDigits dataset. The C3-SKI technique was also applied to the AVDigits dataset, showing 85.62% accuracy. In conclusion, the C3-SKI technique could be applied to perform lip reading recognition.

Keywords: concatenated frame images, convolutional neural network, keyframe reduction, keyframe sequence, lip reading

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