Gallery-sensitive single sample face recognition based on domain adaptation

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作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Wen, Yimin;Yi, Haiyang;Fan, Zhigang;Xu, Zhi;Xue, Yun;...

通讯作者：

Yimin Wen

作者机构：

[Wen, Yimin; Xu, Zhi] Guilin Univ Elect Technol, Guangxi Key Lab Image & Graph Intelligent Proc, Guilin 541004, Peoples R China.

[Wen, Yimin; Yi, Haiyang] Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin 541004, Peoples R China.

[Li, Yujian] Guilin Univ Elect Technol, Sch Artificial Intelligence, Guilin 541004, Peoples R China.

[Fan, Zhigang] Zebra Technol China Corp, Shanghai 200122, Peoples R China.

[Xue, Yun] Hunan City Univ, Sch Municipal & Surveying Engn, Yiyang 413000, Peoples R China.

通讯机构：

[Yimin Wen] G

Guangxi Key Laboratory of Image and Graphic Intelligent Processing, Guilin University of Electronic Technology, Guilin 541004, China<&wdkj&>Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China

语种：

英文

关键词：

Domain adaptation;Discriminative analysis;Single sample face recognition;Transfer learning;Gallery-sensitive

期刊：

Neurocomputing

ISSN：

0925-2312

年：

2021

卷：

458

页码：

626-638

DOI：

10.1016/j.neucom.2020.06.136

基金类别：

National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61866007, 61662014, 61876010]; Natural Science Foundation of Guangxi DistrictNational Natural Science Foundation of Guangxi Province [2018GXNSFDA138006]; Guangxi Key Laboratory of Trusted Software [KX201721]; Collaborative Innovation Center of Cloud Computing and Big Data [YD16E12]; Image Intelligent Processing Project of Key Laboratory Fund [GIIP2005]

机构署名：

本校为其他机构

院系归属：

市政与测绘工程学院

摘要：

Taking advantage of labeled auxiliary training data whose distribution is similar to the distribution of the gallery, single sample face recognition (SSFR) has achieved encouraging performance. However, in many real-world applications, it is difficult to collect such an auxiliary training dataset, while it may be easier to collect an unlabeled target training dataset whose distribution is similar to the distribution of the gallery and a labeled source training dataset whose distribution may be different to the distribution of the gal-lery. How can these three datasets be effectively leveraged ...

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Gallery-sensitive single sample face recognition based on domain adaptation

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