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.
CITATION STYLE
Takalkar, M. A., Zhang, H., & Xu, M. (2019). Improving Micro-expression Recognition Accuracy Using Twofold Feature Extraction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11295 LNCS, pp. 652–664). Springer Verlag. https://doi.org/10.1007/978-3-030-05710-7_54
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