Micro-expression motion magnification: Global Lagrangian vs. local Eulerian approaches

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

doi:10.1109/FG.2018.00102

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 proceeding › Conference Paper › Research

28 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 language	English
Title of host publication	Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018
Editors	Sidney D’Mello, Louis‐Philippe Morency, Michel Valstar, Lijun Yin
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	650-656
Number of pages	7
ISBN (Electronic)	9781538623350
ISBN (Print)	9781538623367
DOIs	https://doi.org/10.1109/FG.2018.00102
Publication status	Published - 2018
Externally published	Yes
Event	IEEE International Conference on Automatic Face and Gesture Recognition 2018 - Xi'an, China Duration: 15 May 2018 → 19 May 2018 Conference number: 13th https://fg2018.cse.sc.edu/

Conference

Conference	IEEE International Conference on Automatic Face and Gesture Recognition 2018
Abbreviated title	FG 2018
Country/Territory	China
City	Xi'an
Period	15/05/18 → 19/05/18
Internet address	https://fg2018.cse.sc.edu/

Keywords

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

Access to Document

10.1109/FG.2018.00102

Cite this

Le Ngo, A. C., Johnston, A., Phan, R. C. W., & See, J. (2018). Micro-expression motion magnification: Global Lagrangian vs. local Eulerian approaches. In S. D’Mello, LP. Morency, M. Valstar, & L. Yin (Eds.), Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018 (pp. 650-656). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/FG.2018.00102

Le Ngo, Anh Cat ; Johnston, Alan ; Phan, Raphael C.W. et al. / Micro-expression motion magnification : Global Lagrangian vs. local Eulerian approaches. Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018. editor / Sidney D’Mello ; Louis‐Philippe Morency ; Michel Valstar ; Lijun Yin. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2018. pp. 650-656

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title = "Micro-expression motion magnification: Global Lagrangian vs. local Eulerian approaches",

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.",

keywords = "Eulerian, Lagrangian, Micro-expressions, Motion Magnification, Multi-channel gradient model",

author = "{Le Ngo}, {Anh Cat} and Alan Johnston and Phan, {Raphael C.W.} and John See",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.; IEEE International Conference on Automatic Face and Gesture Recognition 2018, FG 2018 ; Conference date: 15-05-2018 Through 19-05-2018",

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doi = "10.1109/FG.2018.00102",

language = "English",

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booktitle = "Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

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Le Ngo, AC, Johnston, A, Phan, RCW & See, J 2018, Micro-expression motion magnification: Global Lagrangian vs. local Eulerian approaches. in S D’Mello, LP Morency, M Valstar & L Yin (eds), Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018. IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 650-656, IEEE International Conference on Automatic Face and Gesture Recognition 2018, Xi'an, China, 15/05/18. https://doi.org/10.1109/FG.2018.00102

Micro-expression motion magnification: Global Lagrangian vs. local Eulerian approaches. / Le Ngo, Anh Cat; Johnston, Alan; Phan, Raphael C.W. et al.
Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018. ed. / Sidney D’Mello; Louis‐Philippe Morency; Michel Valstar; Lijun Yin. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2018. p. 650-656.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

TY - GEN

T1 - Micro-expression motion magnification

T2 - IEEE International Conference on Automatic Face and Gesture Recognition 2018

AU - Le Ngo, Anh Cat

AU - Johnston, Alan

AU - Phan, Raphael C.W.

AU - See, John

N1 - Conference code: 13th

PY - 2018

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N2 - 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.

AB - 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.

KW - Eulerian

KW - Lagrangian

KW - Micro-expressions

KW - Motion Magnification

KW - Multi-channel gradient model

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M3 - Conference Paper

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SN - 9781538623367

SP - 650

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BT - Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018

A2 - D’Mello, Sidney

A2 - Morency, Louis‐Philippe

A2 - Valstar, Michel

A2 - Yin, Lijun

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

Y2 - 15 May 2018 through 19 May 2018

ER -

Le Ngo AC, Johnston A, Phan RCW, See J. Micro-expression motion magnification: Global Lagrangian vs. local Eulerian approaches. In D’Mello S, Morency LP, Valstar M, Yin L, editors, Proceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2018. p. 650-656 doi: 10.1109/FG.2018.00102

Micro-expression motion magnification: Global Lagrangian vs. local Eulerian approaches

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Keywords

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