Partial NIR-VIS Heterogeneous Face Recognition With Automatic Saliency Search
文献类型:期刊论文
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作者 | Luo, Mandi1,2,3; Ma, Xin1,2,3; Li, Zhihang2,3; Cao, Jie2,3; He, Ran1,2,3 |
刊名 | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY ; IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY |
出版日期 | 2021 ; 2021 |
卷号 | 16页码:5003-5017 |
ISSN号 | 1556-6013 ; 1556-6013 |
关键词 | Face recognition Face recognition Task analysis Visualization Image recognition Lighting Feature extraction Training Heterogeneous face recognition near infrared-visible matching information bottleneck neural architecture search Task analysis Visualization Image recognition Lighting Feature extraction Training Heterogeneous face recognition near infrared-visible matching information bottleneck neural architecture search |
DOI | 10.1109/TIFS.2021.3122072 ; 10.1109/TIFS.2021.3122072 |
通讯作者 | He, Ran(rhe@nlpr.ia.ac.cn) |
英文摘要 | Near-infrared-visual (NIR-VIS) heterogeneous face recognition (HFR) aims to match NIR face images with the corresponding VIS ones. It is a challenging task due to the sensing gaps among different modalities. Occlusions in the input face images make the task extremely complex. To tackle these problems, we present a Saliency Search Network (SSN) to extract domain-invariant identity features. We propose to automatically search the efficient parts of face images in a modality-aware manner, and remove redundant information. Moreover, the searching process is guided by an information bottleneck network, which mitigates the overfitting problems caused by small datasets. Extensive experiments on both complete and partial NIR-VIS HFR on multiple datasets demonstrate the effectiveness and robustness of the proposed method to modality discrepancy and occlusions.; Near-infrared-visual (NIR-VIS) heterogeneous face recognition (HFR) aims to match NIR face images with the corresponding VIS ones. It is a challenging task due to the sensing gaps among different modalities. Occlusions in the input face images make the task extremely complex. To tackle these problems, we present a Saliency Search Network (SSN) to extract domain-invariant identity features. We propose to automatically search the efficient parts of face images in a modality-aware manner, and remove redundant information. Moreover, the searching process is guided by an information bottleneck network, which mitigates the overfitting problems caused by small datasets. Extensive experiments on both complete and partial NIR-VIS HFR on multiple datasets demonstrate the effectiveness and robustness of the proposed method to modality discrepancy and occlusions. |
WOS关键词 | SPECTRAL REGRESSION ; SPECTRAL REGRESSION ; OCCLUSION ; OCCLUSION |
资助项目 | Beijing Natural Science Foundation[JQ18017] ; Beijing Natural Science Foundation[JQ18017] ; National Natural Science Foundation of China[U20A20223] ; National Natural Science Foundation of China[61721004] ; Youth Innovation Promotion Association Chinese Academy of Sciences (CAS)[Y201929] ; National Natural Science Foundation of China[U20A20223] ; National Natural Science Foundation of China[61721004] ; Youth Innovation Promotion Association Chinese Academy of Sciences (CAS)[Y201929] |
WOS研究方向 | Computer Science ; Computer Science ; Engineering ; Engineering |
语种 | 英语 ; 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC ; IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000714713000001 ; WOS:000714713000001 |
资助机构 | Beijing Natural Science Foundation ; Beijing Natural Science Foundation ; National Natural Science Foundation of China ; Youth Innovation Promotion Association Chinese Academy of Sciences (CAS) ; National Natural Science Foundation of China ; Youth Innovation Promotion Association Chinese Academy of Sciences (CAS) |
源URL | [http://ir.ia.ac.cn/handle/173211/46335] |
专题 | 自动化研究所_智能感知与计算研究中心 |
通讯作者 | He, Ran |
作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China 2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Ctr Res Intelligent Percept & Comp, Beijing 100864, Peoples R China 3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China |
推荐引用方式 GB/T 7714 | Luo, Mandi,Ma, Xin,Li, Zhihang,et al. Partial NIR-VIS Heterogeneous Face Recognition With Automatic Saliency Search, Partial NIR-VIS Heterogeneous Face Recognition With Automatic Saliency Search[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2021, 2021,16, 16:5003-5017, 5003-5017. |
APA | Luo, Mandi,Ma, Xin,Li, Zhihang,Cao, Jie,&He, Ran.(2021).Partial NIR-VIS Heterogeneous Face Recognition With Automatic Saliency Search.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,16,5003-5017. |
MLA | Luo, Mandi,et al."Partial NIR-VIS Heterogeneous Face Recognition With Automatic Saliency Search".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 16(2021):5003-5017. |
入库方式: OAI收割
来源:自动化研究所
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