中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 多媒体计算与图形学团队
A Unified Deep Model for Joint Facial Expression Recognition, Face Synthesis, and Face Alignment

文献类型：期刊论文

作者Zhang, Feifei4,5; Zhang, Tianzhu1,4; Mao, Qirong3; Xu, Changsheng1,2,4
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2020
卷号29页码:6574-6589
关键词Face Task analysis Face recognition Geometry Feature extraction Training Generators Facial expression recognition facial image synthesis generative adversarial network facial landmarks
ISSN号1057-7149
DOI10.1109/TIP.2020.2991549
通讯作者Xu, Changsheng(csxu@nlpr.ia.ac.cn)
英文摘要Facial expression recognition, face synthesis, and face alignment are three coherently related tasks and can be solved in a joint framework. To achieve this goal, in this paper, we propose a novel end-to-end deep learning model by exploiting the expression code, geometry code and generated data jointly for simultaneous pose-invariant facial expression recognition, face image synthesis, and face alignment. The proposed deep model enjoys several merits. First, to the best of our knowledge, this is the first work to address these three tasks jointly in a unified deep model to complement and enhance each other. Second, the proposed model can effectively disentangle the global and local identity representation from different expression and geometry codes. As a result, it can automatically generate facial images with different expressions under arbitrary geometry codes. Third, these three tasks can further boost their performance for each other via our model. Extensive experimental results on three standard benchmarks demonstrate that the proposed deep model performs favorably against state-of-the-art methods on the three tasks.
WOS关键词GAUSSIAN-PROCESSES ; MULTIVIEW ; POSE
资助项目National Key Research and Development Program of China[2017YFB1002804] ; National Natural Science Foundation of China (NSFC)[61720106006] ; National Natural Science Foundation of China (NSFC)[61721004] ; National Natural Science Foundation of China (NSFC)[61832002] ; National Natural Science Foundation of China (NSFC)[61532009] ; National Natural Science Foundation of China (NSFC)[U1705262] ; National Natural Science Foundation of China (NSFC)[U1836220] ; National Natural Science Foundation of China (NSFC)[61702511] ; National Natural Science Foundation of China (NSFC)[61672267] ; National Natural Science Foundation of China (NSFC)[61751211] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC039] ; National Postdoctoral Program for Innovative Talents[BX20190367]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000545079400015
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China (NSFC) ; Key Research Program of Frontier Sciences, CAS ; National Postdoctoral Program for Innovative Talents
源URL[http://ir.ia.ac.cn/handle/173211/40013]  
专题自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
通讯作者Xu, Changsheng
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Peng Cheng Lab, Shenzhen 518066, Peoples R China
3.Jiangsu Univ, Sch Comp Sci & Commun Engn, Zhenjiang 212000, Jiangsu, Peoples R China
4.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
5.Jiangsu Univ, Zhenjiang 212000, Jiangsu, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Feifei,Zhang, Tianzhu,Mao, Qirong,et al. A Unified Deep Model for Joint Facial Expression Recognition, Face Synthesis, and Face Alignment[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2020,29:6574-6589.
APA Zhang, Feifei,Zhang, Tianzhu,Mao, Qirong,&Xu, Changsheng.(2020).A Unified Deep Model for Joint Facial Expression Recognition, Face Synthesis, and Face Alignment.IEEE TRANSACTIONS ON IMAGE PROCESSING,29,6574-6589.
MLA Zhang, Feifei,et al."A Unified Deep Model for Joint Facial Expression Recognition, Face Synthesis, and Face Alignment".IEEE TRANSACTIONS ON IMAGE PROCESSING 29(2020):6574-6589.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。