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Towards Micro-expression Recognition Through Pyramid of Uniform Temporal Local Binary Pattern Features

  • Taoufik Ben AbdallahEmail author
  • Radhouane Guermazi
  • Mohamed Hammami
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)

Abstract

Compared to macro-expressions, recognizing micro-expres-sions is more challenging due to low intensity and their brief duration. To deal with this issue, the present paper proposes a facial micro-expression recognition approach based on the pyramid of uniform Temporal Local Binary Pattern (PTLBP\(^{u2}\)) features for describing the appearance motion changes in time through video stream. Unlike the majority of approaches that use a high dimensional feature space, the proposed approach is based on a low dimensional space with only 83 features. Compared to the most recent facial micro-expression recognition approaches, our approach proves its effectiveness with an accuracy rate reaching 66.40% on Casme II dataset. A study of the ability of a macro-expression model to recognize micro-expression shows that it is more efficient to recognize certain micro-expressions than others.

Keywords

Micro-expressions PTLBP\(^{u2}\) Pyramid representation Low-dimensional feature space Random Forests (RF) Macro-expressions 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Taoufik Ben Abdallah
    • 1
    Email author
  • Radhouane Guermazi
    • 2
  • Mohamed Hammami
    • 3
  1. 1.Faculty of Economics and Management, MIR@CLUniversity of SfaxSfaxTunisia
  2. 2.Saudi Electronic UniversityRiyadhKingdom of Saudi Arabia
  3. 3.Faculty of Sciences, MIR@CLUniversity of SfaxSfaxTunisia

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