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Large-Scale Bisample Learning on ID Versus Spot Face Recognition

文献类型：期刊论文

作者Zhu, Xiangyu1,2,3; Liu, Hao1,2,3; Lei, Zhen1,2,3; Shi, Hailin1,2; Yang, Fan4; Yi, Dong5; Qi, Guojun6; Li, Stan Z.1,2,3
刊名INTERNATIONAL JOURNAL OF COMPUTER VISION
出版日期2019-06-01
卷号127期号:6-7页码:684-700
ISSN号0920-5691
关键词Face recognition ID versus spot Large-scale bisample learning Dominant prototype softmax
DOI10.1007/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
源URL[http://ir.ia.ac.cn/handle/173211/24236]  
专题自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心
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.

入库方式： OAI收割

来源：自动化研究所

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