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Paper Detail

Paper IDMLR-APPL-IVSMR-3.13
Paper Title MICRO-EXPRESSION RECOGNITION BASED ON VIDEO MOTION MAGNIFICATION AND PRE-TRAINED NEURAL NETWORK
Authors Mengjiong Bai, Roland Goecke, Damith Herath, University of Canberra, Australia
SessionMLR-APPL-IVSMR-3: Machine learning for image and video sensing, modeling and representation 3
LocationArea D
Session Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Time:Wednesday, 22 September, 14:30 - 16:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video sensing, modeling, and representation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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.