Two-Stream Deep Correlation Network for Frontal Face Recovery

doi:10.1109/LSP.2017.2736542

	Two-Stream Deep Correlation Network for Frontal Face Recovery
	Zhang, Ting 1,2; Dong, Qiulei1,2,3 ; Tang, Ming1 ; Hu, Zhanyi1,2,3
发表期刊	IEEE SIGNAL PROCESSING LETTERS
	2017-10-01
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
DOI	10.1109/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	多模态人工智能系统全国重点实验室_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
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
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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文件名称/大小	文献类型	版本类型	开放类型	使用许可
Two-Stream Deep Corr（375KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	浏览