Knowledge Commons of Institute of Automation,CAS
Large-Scale Bisample Learning on ID Versus Spot Face Recognition | |
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 | |
发表期刊 | INTERNATIONAL JOURNAL OF COMPUTER VISION |
ISSN | 0920-5691 |
2019-06-01 | |
卷号 | 127期号:6-7页码:684-700 |
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. |
关键词 | Face recognition ID versus spot Large-scale bisample learning Dominant prototype softmax |
DOI | 10.1007/s11263-019-01162-8 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | AuthenMetric RD Funds ; Chinese National Natural Science Foundation[61876178] ; Chinese National Natural Science Foundation[61806196] ; National Key Research and Development Plan[2016YFC080-1002] ; AuthenMetric RD Funds ; Chinese National Natural Science Foundation[61876178] ; Chinese National Natural Science Foundation[61806196] ; National Key Research and Development Plan[2016YFC080-1002] |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000468525900009 |
出版者 | SPRINGER |
七大方向——子方向分类 | 生物特征识别 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/24237 |
专题 | 多模态人工智能系统全国重点实验室_生物识别与安全技术 |
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 |
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 | 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. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Large-scale bisample(2007KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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