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MIFAD-Net: Multi-Layer Interactive Feature Fusion Network With Angular Distance Loss for Face Emotion Recognition

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成果类型:
期刊论文
作者:
Cai, Weiwei;Gao, Ming;Liu, Runmin;Mao, Jie*
通讯作者:
Mao, Jie
作者机构:
[Liu, Runmin; Mao, Jie; Cai, Weiwei] Wuhan Sports Univ, Coll Sports Engn & Informat Technol, Wuhan, Peoples R China.
[Cai, Weiwei] Cent South Univ Forestry & Technol, Sch Logist & Transportat, Changsha, Peoples R China.
[Gao, Ming] Wuhan Sports Univ, Coll Sports Sci & Technol, Wuhan, Peoples R China.
通讯机构:
[Mao, Jie] W
Wuhan Sports Univ, Coll Sports Engn & Informat Technol, Wuhan, Peoples R China.
语种:
英文
关键词:
face emotion;emotion recognition;Multi-layer Interactive;Feature fusion;deep learning;neural networks
期刊:
FRONTIERS IN PSYCHOLOGY
ISSN:
1664-1078
年:
2021
卷:
12
页码:
762795
基金类别:
Scientific Research Program of Education Department of Hubei Province, China [D20184101]; Higher Education Reform Project of Hubei Province, China [201707]; East Lake Scholar of Wuhan Sport University Fund, China and Hubei Provincial University Specialty subject group construction Special fund, China; Hubei Provincial University Specialty subject group construction Special fund, China
机构署名:
本校为其他机构
院系归属:
交通运输与物流学院
摘要:
Understanding human emotions and psychology is a critical step toward realizing artificial intelligence, and correct recognition of facial expressions is essential for judging emotions. However, the differences caused by changes in facial expression are very subtle, and different expression features are less distinguishable, making it difficult for computers to recognize human facial emotions accurately. Therefore, this paper proposes a novel multi-layer interactive feature fusion network model with angular distance loss. To begin, a multi-layer and multi-scale module is designed to extract gl...

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