Acta Electronica Sinica ›› 2022, Vol. 50 ›› Issue (8): 2003-2017.DOI: 10.12263/DZXB.20210639
• SURVEYS AND REVIEWS • Previous Articles Next Articles
SHAO Zhi-wen1,2, ZHOU Yong1,2, TAN Xin3, MA Li-zhuang3,4, LIU Bing1,2, YAO Rui1,2
Received:
2021-05-18
Revised:
2021-09-16
Published:
2022-08-25
Corresponding author:
Supported by:
邵志文1,2, 周勇1,2, 谭鑫3, 马利庄3,4, 刘兵1,2, 姚睿1,2
通讯作者:
作者简介:
基金资助:
CLC Number:
SHAO Zhi-wen, ZHOU Yong, TAN Xin, MA Li-zhuang, LIU Bing, YAO Rui. Survey of Expression Action Unit Recognition Based on Deep Learning[J]. Acta Electronica Sinica, 2022, 50(8): 2003-2017.
邵志文, 周勇, 谭鑫, 马利庄, 刘兵, 姚睿. 基于深度学习的表情动作单元识别综述[J]. 电子学报, 2022, 50(8): 2003-2017.
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整体表情 | 关联的AU |
---|---|
快乐 | 6, 12, 25 |
惊讶 | 1, 2, 5, 26, 27 |
愤怒 | 4, 5, 7, 10, 17, 22, 23, 24, 25, 26 |
厌恶 | 9, 10, 16, 17, 25, 26 |
悲伤 | 1, 4, 6, 11, 15, 17 |
恐惧 | 1, 2, 4, 5, 20, 25, 26, 27 |
整体表情 | 关联的AU |
---|---|
快乐 | 6, 12, 25 |
惊讶 | 1, 2, 5, 26, 27 |
愤怒 | 4, 5, 7, 10, 17, 22, 23, 24, 25, 26 |
厌恶 | 9, 10, 16, 17, 25, 26 |
悲伤 | 1, 4, 6, 11, 15, 17 |
恐惧 | 1, 2, 4, 5, 20, 25, 26, 27 |
微表情 | AU组合 |
---|---|
I | 6, 12, 6+12, 6+7+12, 7+12 |
II | 1+2, 5, 25, 1+2+25, 25+26, 5+24 |
III | 23, 4, 4+7, 4+5, 4+5+7, 17+24, 4+6+7, 4+38 |
IV | 10, 9, 4+9, 4+40, 4+5+40, 4+7+9, 4 +9+17, 4+7+10, 4+5+7+9, 7+10 |
V | 1, 15, 1+4, 6+15, 15+17 |
VI | 1+2+4, 20 |
VII | 其他AU组合 |
微表情 | AU组合 |
---|---|
I | 6, 12, 6+12, 6+7+12, 7+12 |
II | 1+2, 5, 25, 1+2+25, 25+26, 5+24 |
III | 23, 4, 4+7, 4+5, 4+5+7, 17+24, 4+6+7, 4+38 |
IV | 10, 9, 4+9, 4+40, 4+5+40, 4+7+9, 4 +9+17, 4+7+10, 4+5+7+9, 7+10 |
V | 1, 15, 1+4, 6+15, 15+17 |
VI | 1+2+4, 20 |
VII | 其他AU组合 |
数据集 | 采集环境 | 表情激发方式 | 人脸身份数 | 图片或视频数 | 数据形式 | 发布年份 |
---|---|---|---|---|---|---|
CK[ | 受控 | 人为 | 97 | 486个视频 | 2D | 2000 |
BP4D[ | 受控 | 自发 | 41 | 328个视频 | 2D, 3D | 2014 |
DISFA[ | 受控 | 自发 | 27 | 27个视频 | 2D | 2013 |
EmotioNet[ | 非受控 | 自发 | — | 约975 000张图片 | 2D | 2016 |
Aff-Wild2 (AU Set)[ | 非受控 | 自发 | 63 | 63个视频 | 2D | 2019 |
CK+[ | 受控 | 自发 | 26 | 107个视频 | 2D | 2010 |
MMI[ | 受控 | 人为、自发 | 67 | 2 390个视频和493张图片 | 2D | 2005, 2010 |
Bosphorous[ | 受控 | 人为 | 105 | 4 652张图片 | 3D | 2008 |
ICT-3DRFE[ | 受控 | 人为 | 23 | 345张图片 | 2D, 3D | 2011 |
D3DFACS[ | 受控 | 人为 | 10 | 519个视频 | 2D, 3D | 2011 |
BP4D+[ | 受控 | 自发 | 140 | 1 400个视频 | 2D, 3D | 2016 |
GFT[ | 受控 | 自发 | 96 | 96个视频 | 2D | 2017 |
CASME II[ | 受控 | 自发 | 35 | 247个视频 | 2D | 2014 |
SAMM[ | 受控 | 自发 | 32 | 159个视频 | 2D | 2018 |
MMEW[ | 受控 | 自发 | 30 | 300个视频和900张图片 | 2D | 2021 |
数据集 | 采集环境 | 表情激发方式 | 人脸身份数 | 图片或视频数 | 数据形式 | 发布年份 |
---|---|---|---|---|---|---|
CK[ | 受控 | 人为 | 97 | 486个视频 | 2D | 2000 |
BP4D[ | 受控 | 自发 | 41 | 328个视频 | 2D, 3D | 2014 |
DISFA[ | 受控 | 自发 | 27 | 27个视频 | 2D | 2013 |
EmotioNet[ | 非受控 | 自发 | — | 约975 000张图片 | 2D | 2016 |
Aff-Wild2 (AU Set)[ | 非受控 | 自发 | 63 | 63个视频 | 2D | 2019 |
CK+[ | 受控 | 自发 | 26 | 107个视频 | 2D | 2010 |
MMI[ | 受控 | 人为、自发 | 67 | 2 390个视频和493张图片 | 2D | 2005, 2010 |
Bosphorous[ | 受控 | 人为 | 105 | 4 652张图片 | 3D | 2008 |
ICT-3DRFE[ | 受控 | 人为 | 23 | 345张图片 | 2D, 3D | 2011 |
D3DFACS[ | 受控 | 人为 | 10 | 519个视频 | 2D, 3D | 2011 |
BP4D+[ | 受控 | 自发 | 140 | 1 400个视频 | 2D, 3D | 2016 |
GFT[ | 受控 | 自发 | 96 | 96个视频 | 2D | 2017 |
CASME II[ | 受控 | 自发 | 35 | 247个视频 | 2D | 2014 |
SAMM[ | 受控 | 自发 | 32 | 159个视频 | 2D | 2018 |
MMEW[ | 受控 | 自发 | 30 | 300个视频和900张图片 | 2D | 2021 |
方法 | 基于已有 模型的 迁移学习 | 基于已有 标签的 迁移学习 | 基于 域映射的 迁移学习 | 特征点 辅助的 区域学习 | 自适应 区域学习 | 像素级 关联学习 | AU级 关联学习 | 时域 关联学习 | BP4D[ |
---|---|---|---|---|---|---|---|---|---|
DRML[ | √ | 0.483/0.267 | |||||||
EAC-Net[ | √ | √ | √ | 0.559/0.485 | |||||
R-T1[ | √ | √ | √ | √ | 0.661/0.513 | ||||
DSIN[ | √ | √ | 0.589/0.536 | ||||||
LP-Net[ | √ | √ | √ | 0.610/0.569 | |||||
MLCR[ | √ | √ | 0.598/— | ||||||
SRERL[ | √ | √ | √ | 0.629/0.559 | |||||
ARL[ | √ | √ | √ | 0.611/0.587 | |||||
PAttNet[ | √ | 0.626/— | |||||||
D-PAttNet[ | √ | √ | 0.641/— | ||||||
TAE[ | √ | √ | 0.603/0.515 | ||||||
OF-Net[ | √ | √ | 0.597/0.537 | ||||||
AU R-CNN[ | √ | √ | 0.630/0.513 | ||||||
AU-GCN[ | √ | √ | 0.628/0.550 | ||||||
JÂA-Net[ | √ | √ | √ | 0.624/0.635 | |||||
UGN-B[ | √ | √ | 0.633/0.600 | ||||||
Transformer[ | √ | √ | √ | 0.642/0.615 | |||||
HMP-PS[ | √ | √ | 0.634/0.610 |
方法 | 基于已有 模型的 迁移学习 | 基于已有 标签的 迁移学习 | 基于 域映射的 迁移学习 | 特征点 辅助的 区域学习 | 自适应 区域学习 | 像素级 关联学习 | AU级 关联学习 | 时域 关联学习 | BP4D[ |
---|---|---|---|---|---|---|---|---|---|
DRML[ | √ | 0.483/0.267 | |||||||
EAC-Net[ | √ | √ | √ | 0.559/0.485 | |||||
R-T1[ | √ | √ | √ | √ | 0.661/0.513 | ||||
DSIN[ | √ | √ | 0.589/0.536 | ||||||
LP-Net[ | √ | √ | √ | 0.610/0.569 | |||||
MLCR[ | √ | √ | 0.598/— | ||||||
SRERL[ | √ | √ | √ | 0.629/0.559 | |||||
ARL[ | √ | √ | √ | 0.611/0.587 | |||||
PAttNet[ | √ | 0.626/— | |||||||
D-PAttNet[ | √ | √ | 0.641/— | ||||||
TAE[ | √ | √ | 0.603/0.515 | ||||||
OF-Net[ | √ | √ | 0.597/0.537 | ||||||
AU R-CNN[ | √ | √ | 0.630/0.513 | ||||||
AU-GCN[ | √ | √ | 0.628/0.550 | ||||||
JÂA-Net[ | √ | √ | √ | 0.624/0.635 | |||||
UGN-B[ | √ | √ | 0.633/0.600 | ||||||
Transformer[ | √ | √ | √ | 0.642/0.615 | |||||
HMP-PS[ | √ | √ | 0.634/0.610 |
方法 | 基于已有 模型的 迁移学习 | 基于已有 标签的 迁移学习 | 基于 域映射的 迁移学习 | 特征点 辅助的 区域学习 | 自适应 区域学习 | 像素级 关联学习 | AU级 关联学习 | 时域 关联学习 | BP4D[ |
---|---|---|---|---|---|---|---|---|---|
DRML[ | √ | 0.52/0.29 | |||||||
CCNN-IT[ | √ | 0.63/0.45 | |||||||
2DC[ | √ | 0.66/0.50 | |||||||
KBSS[ | √ | √ | 0.67/0.36 | ||||||
ARL[ | √ | √ | 0.66/0.48 | ||||||
SCC[ | √ | √ | 0.72/0.47 | ||||||
G2RL[ | √ | √ | √ | 0.69/0.52 | |||||
DPG[ | √ | √ | 0.72/0.56 |
方法 | 基于已有 模型的 迁移学习 | 基于已有 标签的 迁移学习 | 基于 域映射的 迁移学习 | 特征点 辅助的 区域学习 | 自适应 区域学习 | 像素级 关联学习 | AU级 关联学习 | 时域 关联学习 | BP4D[ |
---|---|---|---|---|---|---|---|---|---|
DRML[ | √ | 0.52/0.29 | |||||||
CCNN-IT[ | √ | 0.63/0.45 | |||||||
2DC[ | √ | 0.66/0.50 | |||||||
KBSS[ | √ | √ | 0.67/0.36 | ||||||
ARL[ | √ | √ | 0.66/0.48 | ||||||
SCC[ | √ | √ | 0.72/0.47 | ||||||
G2RL[ | √ | √ | √ | 0.69/0.52 | |||||
DPG[ | √ | √ | 0.72/0.56 |
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