Large-Scale Bisample Learning on ID Versus Spot Face Recognition

doi:10.1007/s11263-019-01162-8

	Large-Scale Bisample Learning on ID Versus Spot Face Recognition
	Zhu, Xiangyu1,2,3 ; Liu, Hao 1,2,3; Lei, Zhen1,2,3 ; Shi, Hailin1,2 ; Yang, Fan 4; Yi, Dong 5; Qi, Guojun 6; Li, Stan Z.1,2,3
刊名	INTERNATIONAL JOURNAL OF COMPUTER VISION
	2019-06-01
卷号	127 期号:6-7 页码:684-700
关键词	Face recognition ID versus spot Large-scale bisample learning Dominant prototype softmax
ISSN号	0920-5691
DOI	10.1007/s11263-019-01162-8
通讯作者	Lei, Zhen(zlei@nlpr.ia.ac.cn)
英文摘要	In real-world face recognition applications, there is a tremendous amount of data with two images for each person. One is an ID photo for face enrollment, and the other is a probe photo captured on spot. Most existing methods are designed for training data with limited breadth (a relatively small number of classes) and sufficient depth (many samples for each class). They would meet great challenges on ID versus Spot (IvS) data, including the under-represented intra-class variations and an excessive demand on computing devices. In this paper, we propose a deep learning based large-scale bisample learning (LBL) method for IvS face recognition. To tackle the bisample problem with only two samples for each class, a classification-verification-classification training strategy is proposed to progressively enhance the IvS performance. Besides, a dominant prototype softmax is incorporated to make the deep learning scalable on large-scale classes. We conduct LBL on a IvS face dataset with more than two million identities. Experimental results show the proposed method achieves superior performance to previous ones, validating the effectiveness of LBL on IvS face recognition.
资助项目	Chinese National Natural Science Foundation[61876178] ; Chinese National Natural Science Foundation[61806196] ; National Key Research and Development Plan[2016YFC080-1002] ; AuthenMetric RD Funds
WOS研究方向	Computer Science
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000468525900009
资助机构	Chinese National Natural Science Foundation ; National Key Research and Development Plan ; AuthenMetric RD Funds
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/24236]
专题	中国科学院自动化研究所
通讯作者	Lei, Zhen
作者单位	1.Chinese Acad Sci, Ctr Biometr & Secur Res, Beijing, Peoples R China 2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Beihang Univ, Coll Software, Beijing, Peoples R China 5.DAMO Acad, Alibaba Grp, Hangzhou, Zhejiang, Peoples R China 6.HUAWEI Cloud, Boston, MA USA
推荐引用方式 GB/T 7714	Zhu, Xiangyu,Liu, Hao,Lei, Zhen,et al. Large-Scale Bisample Learning on ID Versus Spot Face Recognition[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2019,127(6-7):684-700.
APA	Zhu, Xiangyu.,Liu, Hao.,Lei, Zhen.,Shi, Hailin.,Yang, Fan.,...&Li, Stan Z..(2019).Large-Scale Bisample Learning on ID Versus Spot Face Recognition.INTERNATIONAL JOURNAL OF COMPUTER VISION,127(6-7),684-700.
MLA	Zhu, Xiangyu,et al."Large-Scale Bisample Learning on ID Versus Spot Face Recognition".INTERNATIONAL JOURNAL OF COMPUTER VISION 127.6-7(2019):684-700.