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Detecting Micro-expression Intensity Changes from Videos Based on Hybrid Deep CNN

  • Selvarajah ThuseethanEmail author
  • Sutharshan Rajasegarar
  • John Yearwood
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11441)

Abstract

Facial micro-expressions, which usually last only for a fraction of a second, are challenging to detect by the human eye or machine. They are useful for understanding the genuine emotional state of a human face, and have various applications in education, medical, surveillance and legal sectors. Existing works on micro-expressions are focused on binary classification of the micro-expressions. However, detecting the micro-expression intensity changes over the spanning time, i.e., the micro-expression profiling, is not addressed in the literature. In this paper, we present a novel deep Convolutional Neural Network (CNN) based hybrid framework for micro-expression intensity change detection together with an image pre-processing technique. The two components of our hybrid framework, namely a micro-expression stage classifier, and an intensity estimator, are designed using a 3D and 2D shallow deep CNNs respectively. Moreover, we propose a fusion mechanism to improve the micro-expression intensity classification accuracy. Evaluation using the recent benchmark micro-expression datasets; CASME, CASME II and SAMM, demonstrates that our hybrid framework can accurately classify the various intensity levels of each micro-expression. Further, comparison with the state-of-the-art methods reveals the superiority of our hybrid approach in classifying the micro-expressions accurately.

Keywords

Micro-expression intensity Convolutional Neural Networks Hybrid framework Fusion mechanism 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Selvarajah Thuseethan
    • 1
    Email author
  • Sutharshan Rajasegarar
    • 1
  • John Yearwood
    • 1
  1. 1.Deakin UniversityGeelongAustralia

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