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Two-Stream Deep Correlation Network for Frontal Face Recovery
Zhang, Ting1,2; Dong, Qiulei1,2,3; Tang, Ming1; Hu, Zhanyi1,2,3
2017-10-01
发表期刊IEEE SIGNAL PROCESSING LETTERS
卷号24期号:10页码:1478-1482
文章类型Article
摘要Pose and textural variations are two dominant factors to affect the performance of face recognition. It is widely believed that generating the corresponding frontal face froma face image of an arbitrary pose is an effective step toward improving the recognition performance. In the literature, however, the frontal face is generally recovered by only exploring textural characteristic. In this letter, we propose a two-stream deep correlation network, which incorporates both geometric and textural features for frontal face recovery. Given a face image under an arbitrary pose as input, geometric and textural characteristics are first extracted from two separate streams. The extracted characteristics are then fused through the proposed multiplicative patch correlation layer. These two steps are integrated into one network for end-to-end training and prediction, which is demonstrated effective compared with state-of-the-art methods on the benchmark datasets.
关键词Correlation Layer Deep Neural Network Frontal Face Recovery Geometric Stream Textural Stream
WOS标题词Science & Technology ; Technology
DOI10.1109/LSP.2017.2736542
关键词[WOS]RECOGNITION ; IDENTITY ; SPACE ; MODEL
收录类别SCI
语种英语
项目资助者Strategic Priority Research Program of the Chinese Academy of Sciences(XDB02070002) ; National Natural Science Foundation of China(61421004 ; 61375042 ; 61573359)
WOS研究方向Engineering
WOS类目Engineering, Electrical & Electronic
WOS记录号WOS:000408775600006
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/19712
专题模式识别国家重点实验室_机器人视觉
通讯作者Dong, Qiulei
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714
Zhang, Ting,Dong, Qiulei,Tang, Ming,et al. Two-Stream Deep Correlation Network for Frontal Face Recovery[J]. IEEE SIGNAL PROCESSING LETTERS,2017,24(10):1478-1482.
APA Zhang, Ting,Dong, Qiulei,Tang, Ming,&Hu, Zhanyi.(2017).Two-Stream Deep Correlation Network for Frontal Face Recovery.IEEE SIGNAL PROCESSING LETTERS,24(10),1478-1482.
MLA Zhang, Ting,et al."Two-Stream Deep Correlation Network for Frontal Face Recovery".IEEE SIGNAL PROCESSING LETTERS 24.10(2017):1478-1482.
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