Micro-expression motion magnification: Global lagrangian vs. local eulerian approaches

Anh Cat Le Ngo, Alan Johnston, Raphael C. W. Phan, John See

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

Micro-expressions are difficult to spot but are utterly important for engaging in a conversation or negotiation. Through motion magnification, these expressions become much more distinguishable and easily recognized. This work proposes Global Lagrangian Motion Magnification (GLMM) for consistent exaggeration of facial expressions and dynamics across a whole video. As the proposal takes an opposite approach to a previous pivotal work, i.e. local Amplitude-based Eulerian Motion Magnification (AEMM). GLMM and AEMM are theoretically analyzed for potential advantages and disadvantages, especially with respect to how magnified noise and distortions are dealt with. Then, both GLMM and AEMM are empirically evaluated and compared using the CASME II micro-expression corpus.

Original languageEnglish
Title of host publication13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)
PublisherIEEE
Pages650-656
Number of pages7
ISBN (Electronic)9781538623350
DOIs
Publication statusPublished - 7 Jun 2018
Event13th IEEE International Conference on Automatic Face and Gesture Recognition 2018 - Xi'an, China
Duration: 15 May 201819 May 2018

Conference

Conference13th IEEE International Conference on Automatic Face and Gesture Recognition 2018
Abbreviated titleFG 2018
Country/TerritoryChina
CityXi'an
Period15/05/1819/05/18

Keywords

  • Eulerian
  • Lagrangian
  • Micro-expressions
  • Motion Magnification
  • Multi-channel gradient model

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Control and Optimization

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