CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Joint face alignment and segmentation via deep multi-task learning
Zhao, Yucheng1,2; Tang, Fan1,2; Dong, Weiming1; Huang, Feiyue3; Zhang, Xiaopeng1
Source PublicationMULTIMEDIA TOOLS AND APPLICATIONS
ISSN1380-7501
2019-05-01
Volume78Issue:10Pages:13131-13148
Corresponding AuthorDong, Weiming(weiming.dong@ia.ac.cn)
AbstractFace alignment and segmentation are challenging problems which have been extensively studied in the field of multimedia. These two tasks are closely related and their learning processes are supposed to benefit each other. Hence, we present a joint multi-task learning algorithm for both face alignment and segmentation using deep convolutional neural network (CNN). The proposed multi-task learning approach allows CNN model to simultaneously share visual knowledge between different tasks. With a carefully designed refinement residual module, the cross-layer features are fused in a collaborative manner. To the best of our knowledge, this is the first time that face alignment and segmentation are learned together via deep multi-task learning. Our experiments show that learning these two related tasks simultaneously builds a synergy between them, improves the performance of each individual task, and rivals recent approaches. Furthermore, we demonstrate the effectiveness of our model in two practical applications: virtual makeup and face swap.
KeywordFace alignment Face segmentation Multi-task learning Virtual makeup Face swap
DOI10.1007/s11042-018-5609-1
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61672520] ; National Natural Science Foundation of China[61702488] ; National Natural Science Foundation of China[61501464] ; National Natural Science Foundation of China[6120106003] ; Beijing Natural Science Foundation[4162056] ; National Key Technology R&D Program of China[2015BAH53F02] ; CASIA-Tencent YouTu jointly research project
Funding OrganizationNational Natural Science Foundation of China ; Beijing Natural Science Foundation ; National Key Technology R&D Program of China ; CASIA-Tencent YouTu jointly research project
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000471654900022
PublisherSPRINGER
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20889
Collection模式识别国家重点实验室_多媒体计算与图形学
Corresponding AuthorDong, Weiming
Affiliation1.Chinese Acad Sci, Inst Automat, NLPR LIAMA, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Tencent, YouTu Lab, Shanghai, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhao, Yucheng,Tang, Fan,Dong, Weiming,et al. Joint face alignment and segmentation via deep multi-task learning[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2019,78(10):13131-13148.
APA Zhao, Yucheng,Tang, Fan,Dong, Weiming,Huang, Feiyue,&Zhang, Xiaopeng.(2019).Joint face alignment and segmentation via deep multi-task learning.MULTIMEDIA TOOLS AND APPLICATIONS,78(10),13131-13148.
MLA Zhao, Yucheng,et al."Joint face alignment and segmentation via deep multi-task learning".MULTIMEDIA TOOLS AND APPLICATIONS 78.10(2019):13131-13148.
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