Eulerian emotion magnification for subtle expression recognition

Anh Cat Le Ngo, Yee Hui Oh, Raphael C. W. Phan, John See

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

31 Citations (Scopus)

Abstract

Subtle emotions are expressed through tiny and brief movements of facial muscles, called micro-expressions; thus, recognition of these hidden expressions is as challenging as inspection of microscopic worlds without microscopes. In this paper, we show that through motion magnification, subtle expressions can be realistically exaggerated and become more easily recognisable. We magnify motions of facial expressions in the Eulerian perspective by manipulating their amplitudes or phases. To evaluate effects of exaggerating facial expressions, we use a common framework (LBP-TOP features and SVM classifiers) to perform 5-class subtle emotion recognition on the CASME II corpus, a spontaneous subtle emotion database. According to experimental results, significant improvements in recognition rates of magnified micro-expressions over normal ones are confirmed and measured. Furthermore, we estimate upper bounds of effective magnification factors and empirically corroborate these theoretical calculations with experimental data.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Pages1243-1247
Number of pages5
ISBN (Electronic)9781479999880
DOIs
Publication statusPublished - 19 May 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing 2016 - Shanghai International Convention Center, Shanghai, China
Duration: 20 Mar 201625 Mar 2016

Conference

Conference41st IEEE International Conference on Acoustics, Speech and Signal Processing 2016
Abbreviated titleICASSP 2016
Country/TerritoryChina
CityShanghai
Period20/03/1625/03/16

Keywords

  • Classification
  • Micro-expression recognition
  • Motion magnification
  • Subtle emotion

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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