Chunlong Hu1,*, Jianjun Chen1, Xin Zuo1, Haitao Zou1, Xing Deng1, Yucheng Shu2
CMES-Computer Modeling in Engineering & Sciences, Vol.118, No.3, pp. 547-559, 2019, DOI:10.31614/cmes.2019.04032
Abstract Micro-expression recognition has attracted growing research interests in the field of compute vision. However, micro-expression usually lasts a few seconds, thus it is difficult to detect. This paper presents a new framework to recognize micro-expression using pyramid histogram of Centralized Gabor Binary Pattern from Three Orthogonal Panels (CGBP-TOP) which is an extension of Local Gabor Binary Pattern from Three Orthogonal Panels feature. CGBP-TOP performs spatial and temporal analysis to capture the local facial characteristics of micro-expression image sequences. In order to keep more local information of the face, CGBP-TOP is extracted based on pyramid sub-regions of the micro-expression video frame.… More >
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