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http://bura.brunel.ac.uk/handle/2438/17185
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DC Field | Value | Language |
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dc.contributor.author | Zhang, Z | - |
dc.contributor.author | Chen, T | - |
dc.contributor.author | Meng, H | - |
dc.contributor.author | Liu, G | - |
dc.contributor.author | Fu, X | - |
dc.date.accessioned | 2018-12-05T15:45:49Z | - |
dc.date.available | 2018-11-05 | - |
dc.date.available | 2018-12-05T15:45:49Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | IEEE Access | en_US |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/17185 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.subject | Spotting Micro-Expression | en_US |
dc.subject | Apex Frame | en_US |
dc.subject | Convolutional Neural Network | en_US |
dc.subject | Deep Learning | en_US |
dc.title | SMEConvNet: A Convolutional Neural Network for Spotting Spontaneous Facial Micro-Expression from Long Videos | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1109/ACCESS.2018.2879485 | - |
dc.relation.isPartOf | IEEE Access | - |
pubs.publication-status | Published | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | 661.98 kB | Adobe PDF | View/Open |
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