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

作     者：Chunlong Hu Jianjun Chen Xin Zuo Haitao Zou Xing Deng Yucheng Shu 

作者机构：School of Computer Science and EngineeringJiangsu University of Science and TechnologyZhenjiang212003Chinal School of Computer Science and TechnologyChongqing University of Posts and TelecommunicationsChongqing400065China 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2019年第118卷第3期

页      面：547-559页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work is funded by the natural science foundation of Jiangsu Province(No.BK20150471) the natural science foundation of the higher education institutions of Jiangsu Province(No.17KJB520007) the Key Research and Development Program of Zhenjiang-Social Development(No.SH2018005) the scientific researching fund of Jiangsu University of Science and Technology(No.1132921402,No.1132931803) the basic science and frontier technology research program of Chongqing Municipal Science and Technology Commission(cstc2016jcyjA0407) 

主　　题：Micro-expression recognition feature extraction spatial pyramid multi-task learning regularization 

摘      要：Micro-expression recognition has attracted growing research interests in the field of compute ***,micro-expression usually lasts a few seconds,thus it is difficult to *** 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 ***-TOP performs spatial and temporal analysis to capture the local facial characteristics of micro-expression image *** order to keep more local information of the face,CGBP-TOP is extracted based on pyramid subregions of the micro-expression video *** combination of CGBP-TOP and spatial pyramid can represent well and truly the facial movements of the micro-expression image ***,the dimension of our pyramid CGBP-TOP tends to be very high,which may lead to high data redundancy *** addition,it is clear that people of different genders usually have different ways of ***,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 ***,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 ***,our proposed approach achieves comparable results with the state-of-the-art methods.