Face recognition on large-scale video in the wild with hybrid Euclidean-and-Riemannian metric learning

doi:10.1016/j.patcog.2015.03.011

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Face recognition on large-scale video in the wild with hybrid Euclidean-and-Riemannian metric learning
	Huang, Zhiwu 1,2; Wang, Ruiping 1; Shan, Shiguang 1; Chen, Xilin 1
刊名	PATTERN RECOGNITION
	2015-10-01
卷号	48 期号:10 页码:3113-3124
关键词	Face recognition Large-scale video Multiple heterogeneous statistics Hybrid Euclidean-and-Riemannian metric learning
ISSN号	0031-3203
DOI	10.1016/j.patcog.2015.03.011
英文摘要	Face recognition on large-scale video in the wild is becoming increasingly important due to the ubiquity of video data captured by surveillance cameras, handheld devices, Internet uploads, and other sources. By treating each video as one image set, set-based methods recently have made great success in the field of video-based face recognition. In the wild world, videos often contain extremely complex data variations and thus pose a big challenge of set modeling for set-based methods. In this paper, we propose a novel Hybrid Euclidean-and-Riemannian Metric Learning (HERML) method to fuse multiple statistics of image set. Specifically, we represent each image set simultaneously by mean, covariance matrix and Gaussian distribution, which generally complement each other in the aspect of set modeling. However, it is not trivial to fuse them since mean, covariance matrix and Gaussian model typically lie in multiple heterogeneous spaces equipped with Euclidean or Riemannian metric. Therefore, we first implicitly map the original statistics into high dimensional Hilbert spaces by exploiting Euclidean and Riemannian kernels. With a LogDet divergence based objective function, the hybrid kernels are then fused by our hybrid metric learning framework, which can efficiently perform the fusing procedure on large-scale videos. The proposed method is evaluated on four public and challenging large-scale video face datasets. Extensive experimental results demonstrate that our method has a clear superiority over the state-of-the-art set-based methods for large-scale video-based face recognition. (C) 2015 Elsevier Ltd. All rights reserved.
资助项目	National Basic Research Program of China (973 Program)[2015CB351802] ; Natural Science Foundation of China[61222211] ; Natural Science Foundation of China[61390511] ; Natural Science Foundation of China[61379083]
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	ELSEVIER SCI LTD
WOS记录号	WOS:000357246100014
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/9620]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Shan, Shiguang
作者单位	1.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Huang, Zhiwu,Wang, Ruiping,Shan, Shiguang,et al. Face recognition on large-scale video in the wild with hybrid Euclidean-and-Riemannian metric learning[J]. PATTERN RECOGNITION,2015,48(10):3113-3124.
APA	Huang, Zhiwu,Wang, Ruiping,Shan, Shiguang,&Chen, Xilin.(2015).Face recognition on large-scale video in the wild with hybrid Euclidean-and-Riemannian metric learning.PATTERN RECOGNITION,48(10),3113-3124.
MLA	Huang, Zhiwu,et al."Face recognition on large-scale video in the wild with hybrid Euclidean-and-Riemannian metric learning".PATTERN RECOGNITION 48.10(2015):3113-3124.