英文论文


文献类型
Journal article (JA)
题名
Micro-Expression Recognition Based on Optical Flow and PCANet
作者
Wang, Shiqi; Guan, Suen; Lin, Hui; Huang, Jianming; Long, Fei; Yao, Junfeng
作者单位
[Wang, Shiqi; Guan, Suen; Lin, Hui; Long, Fei; Yao, Junfeng] Xiamen Univ, Sch Informat, Xiamen 361005, Peoples R China. [Huang, Jianming; Long, Fei; Yao, Junfeng] Xiamen Univ, Ctr Digital Media Comp & Software Engn, Xiamen 361005, Peoples R China.
通讯作者地址
Xiamen Univ, Sch Informat, Xiamen 361005, Peoples R China.; Xiamen Univ, Ctr Digital Media Comp & Software Engn, Xiamen 361005, Peoples R China.
Email
wangshiqi@stu.xmu.edu.cn; gse2020XMU@foxmail.com; 24320182203231@stu.xmu.edu.cn; 24320142202428@stu.xmu.edu.cn; flong@xmu.edu.cn; yao0010@xmu.edu.cn
ResearchID
ORCID
期刊名称
Sensors
出版社
MDPI
ISSN
1424-8220
出版信息
June-1 2022, 22 (11)
JCR
影响因子
ISBN
基金
Natural Science Foundation of Fujian Province of China [2019J01001]; Industry-University-Research Project of Xiamen City [3502Z20203002]
会议名称
会议地点
会议开始日期
会议结束日期
关键词
Deep neural networks
摘要
Micro-expressions are rapid and subtle facial movements. Different from ordinary facial expressions in our daily life, micro-expressions are very difficult to detect and recognize. In recent years, due to a wide range of potential applications in many domains, micro-expression recognition has aroused extensive attention from computer vision. Because available micro-expression datasets are very small, deep neural network models with a huge number of parameters are prone to over-fitting. In this article, we propose an OF-PCANet+ method for micro-expression recognition, in which we design a spatiotemporal feature learning strategy based on shallow PCANet+ model, and we incorporate optical flow sequence stacking with the PCANet+ network to learn discriminative spatiotemporal features. We conduct comprehensive experiments on publicly available SMIC and CASME2 datasets. The results show that our lightweight model obviously outperforms popular hand-crafted methods and also achieves comparable performances with deep learning based methods, such as 3D-FCNN and ELRCN.
一级学科
Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation
WOS入藏号
WOS:000808840300001
EI收录号
20222312199778
DOI
10.3390/s22114296
ESI
收录于
SCIE, EI

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