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2019 | OriginalPaper | Chapter

Improving Micro-expression Recognition Accuracy Using Twofold Feature Extraction

Authors : Madhumita A. Takalkar, Haimin Zhang, Min Xu

Published in: MultiMedia Modeling

Publisher: Springer International Publishing

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Abstract

Micro-expressions are generated involuntarily on a person’s face and are usually a manifestation of repressed feelings of the person. Micro-expressions are characterised by short duration, involuntariness and low intensity. Because of these characteristics, micro-expressions are difficult to perceive and interpret correctly, and they are profoundly challenging to identify and categorise automatically.
Previous work for micro-expression recognition has used hand-crafted features like LBP-TOP, Gabor filter, HOG and optical flow. Recent work also has demonstrated the possible use of deep learning for micro-expression recognition. This paper is the first work to explore the use of hand-craft feature descriptor and deep feature descriptor for micro-expression recognition task. The aim is to use the hand-craft and deep learning feature descriptor to extract features and integrate them together to construct a large feature vector to describe a video. Through experiments on CASME, CASME II and CASME+2 databases, we demonstrate our proposed method can achieve promising results for micro-expression recognition accuracy with larger training samples.

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Metadata
Title
Improving Micro-expression Recognition Accuracy Using Twofold Feature Extraction
Authors
Madhumita A. Takalkar
Haimin Zhang
Min Xu
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-05710-7_54