Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/17185
Title: SMEConvNet: A Convolutional Neural Network for Spotting Spontaneous Facial Micro-Expression from Long Videos
Authors: Zhang, Z
Chen, T
Meng, H
Liu, G
Fu, X
Keywords: Spotting Micro-Expression;Apex Frame;Convolutional Neural Network;Deep Learning
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers
Citation: IEEE Access
Abstract: Micro-expression is a subtle and involuntary facial expression that may reveal the hidden emotion of human beings. Spotting micro-expression means to locate the moment when the microexpression happens, which is a primary step for micro-expression recognition. Previous work in microexpression expression spotting focus on spotting micro-expression from short video, and with hand-crafted features. In this paper, we present a methodology for spotting micro-expression from long videos. Specifically, a new convolutional neural network named as SMEConvNet (Spotting Micro-Expression Convolutional Network) was designed for extracting features from video clips, which is the first time that deep learning is used in micro-expression spotting. Then a feature matrix processing method was proposed for spotting the apex frame from long video, which uses a sliding window and takes the characteristics of micro-expression into account to search the apex frame. Experimental results demonstrate that the proposed method can achieve better performance than existing state-of-art methods.
URI: http://bura.brunel.ac.uk/handle/2438/17185
DOI: http://dx.doi.org/10.1109/ACCESS.2018.2879485
ISSN: 2169-3536
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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