Micro-Expression Motion Magnification: Global Lagrangian vs. Local Eulerian Approaches


Le Ngo, Anh Cat and Johnston, Alan and Phan, Raphael and See, John (2018) Micro-Expression Motion Magnification: Global Lagrangian vs. Local Eulerian Approaches. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), 15-19 May 2018, Xi'an, China.

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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 Amplitudebased 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

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Machine learning, databases
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Engineering (FOE)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 30 Mar 2021 18:15
Last Modified: 30 Mar 2021 18:15
URII: http://shdl.mmu.edu.my/id/eprint/7566


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