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

• 作者: Chunlong Hu Haitao Zou Jianjun Chen Xin Zuo Xing Deng Yucheng Shu
• 来源期刊: 工程与科学中的计算机建模(英文)
• 学科: 工学
• 关键词: Micro-expression recognition FEATURE extraction spatial PYRAMID MULTI-TASK learning REGULARIZATION
• 年，卷（期）: 2019,（3）
• 页码范围: 547-559
• 页数: 13页
• 分类号: TP3

摘要

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 subregions 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.