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MICRO-EXPRESSION RECOGNITION BASED ON VIDEO MOTION MAGNIFICATION AND PRE-TRAINED NEURAL NETWORK

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Citation Author(s):
Mengjiong Bai, Roland Goecke, and Damith Herath
Submitted by:
Mengjiong Bai
Last updated:
15 September 2021 - 7:19am
Document Type:
Presentation Slides
Document Year:
2021
Event:
Presenters Name:
Mengjiong Bai
Paper Code:
3078

Abstract 

Abstract: 

This paper investigates the effects of using video motion magnification methods based on amplitude and phase, respectively, to amplify small facial movements. We hypothesise that this approach will assist in the micro-expression recognition task. To this end, we apply the pre-trained VGGFace2 model with its excellent facial feature capturing ability to transfer learn the magnified micro-expression movement, then encode the spatial information and decode the spatial and temporal information by Bi-LSTM model. Moreover, Grad-CAM is utilised to map the model and visually explain the operating mechanism of the spatio-temporal network. Experiments on the SMIC database confirm that the proposed framework significantly improves the micro-expression recognition rate compared to without video magnification (baseline) and other state-of-the-art methods.

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