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Subtle Expression Recognition Using Optical Strain Weighted Features

  • Sze-Teng LiongEmail author
  • John See
  • Raphael C.-W. Phan
  • Anh Cat Le Ngo
  • Yee-Hui Oh
  • KokSheik Wong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9009)

Abstract

Optical strain characterizes the relative amount of displacement by a moving object within a time interval. Its ability to compute any small muscular movements on faces can be advantageous to subtle expression research. This paper proposes a novel optical strain weighted feature extraction scheme for subtle facial micro-expression recognition. Motion information is derived from optical strain magnitudes, which is then pooled spatio-temporally to obtain block-wise weights for the spatial image plane. By simple product with the weights, the resulting feature histograms are intuitively scaled to accommodate the importance of block regions. Experiments conducted on two recent spontaneous micro-expression databases–CASMEII and SMIC, demonstrated promising improvement over the baseline results.

Keywords

Support Vector Machine Facial Expression Optical Flow Emotion Recognition Local Binary Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sze-Teng Liong
    • 1
    Email author
  • John See
    • 2
  • Raphael C.-W. Phan
    • 3
  • Anh Cat Le Ngo
    • 3
  • Yee-Hui Oh
    • 3
  • KokSheik Wong
    • 1
  1. 1.Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  2. 2.Faculty of Computing and InformaticsMultimedia UniversityCyberjayaMalaysia
  3. 3.Faculty of EngineeringMultimedia UniversityCyberjayaMalaysia

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