Open Access

ARTICLE

Gender-Specific Multi-Task Micro-Expression Recognition Using Pyramid CGBP-TOP Feature

Chunlong Hu^1,*, Jianjun Chen¹, Xin Zuo¹, Haitao Zou¹, Xing Deng¹, Yucheng Shu²

1 School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212003, China.
2 School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.

* Corresponding Author: Chunlong Hu. Email: .

Computer Modeling in Engineering & Sciences 2019, 118(3), 547-559. https://doi.org/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. The combination of CGBP-TOP and spatial pyramid can represent well and truly the facial movements of the micro-expression image sequences. However, the dimension of our pyramid CGBP-TOP tends to be very high, which may lead to high data redundancy problem. In addition, it is clear that people of different genders usually have different ways of micro-expression. Therefore, in this paper, in order to select the relevant features of micro-expression, the gender-specific sparse multi-task learning method with adaptive regularization term is adopted to learn a compact subset of pyramid CGBP-TOP feature for micro-expression classification of different sexes. Finally, extensive experiments on widely used CASME II and SMIC databases demonstrate that our method can efficiently extract micro-expression motion features in the micro-expression video clip. Moreover, our proposed approach achieves comparable results with the state-of-the-art methods.

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Keywords

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