CASIA OpenIR  > 多模态人工智能系统全国重点实验室
Multiscale Dynamic Graph Representation for Biometric Recognition With Occlusions
Ren, Min1; Wang, Yunlong2; Zhu, Yuhao3; Zhang, Kunbo2; Sun, Zhenan2,4
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN0162-8828
2023-12-01
Volume45Issue:12Pages:15120-15136
Corresponding AuthorSun, Zhenan(znsun@nlpr.ia.ac.cn)
AbstractOcclusion is a common problem with biometric recognition in the wild. The generalization ability of CNNs greatly decreases due to the adverse effects of various occlusions. To this end, we propose a novel unified framework integrating the merits of both CNNs and graph models to overcome occlusion problems in biometric recognition, called multiscale dynamic graph representation (MS-DGR). More specifically, a group of deep features reflected on certain subregions is recrafted into a feature graph (FG). Each node inside the FG is deemed to characterize a specific local region of the input sample, and the edges imply the co-occurrence of non-occluded regions. By analyzing the similarities of the node representations and measuring the topological structures stored in the adjacent matrix, the proposed framework leverages dynamic graph matching to judiciously discard the nodes corresponding to the occluded parts. The multiscale strategy is further incorporated to attain more diverse nodes representing regions of various sizes. Furthermore, the proposed framework exhibits a more illustrative and reasonable inference by showing the paired nodes. Extensive experiments demonstrate the superiority of the proposed framework, which boosts the accuracy in both natural and occlusion-simulated cases by a large margin compared with that of baseline methods.
KeywordBiometrics deep learning face recognition graph neural networks iris recognition
DOI10.1109/TPAMI.2023.3298836
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2022YFC3310400] ; National Natural Science Foundation of China[62276025] ; National Natural Science Foundation of China[62276263] ; National Natural Science Foundation of China[62006225] ; National Natural Science Foundation of China[62071468] ; Shenzhen Technology Plan Program[KQTD20170331093217368]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Shenzhen Technology Plan Program
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:001130146400066
PublisherIEEE COMPUTER SOC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/55534
Collection多模态人工智能系统全国重点实验室
Corresponding AuthorSun, Zhenan
Affiliation1.Beijing Normal Univ, Sch Artificial Intelligence, Beijing 100875, Peoples R China
2.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.China Acad Railway Sci, Postgrad Dept, Beijing 100081, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Ren, Min,Wang, Yunlong,Zhu, Yuhao,et al. Multiscale Dynamic Graph Representation for Biometric Recognition With Occlusions[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2023,45(12):15120-15136.
APA Ren, Min,Wang, Yunlong,Zhu, Yuhao,Zhang, Kunbo,&Sun, Zhenan.(2023).Multiscale Dynamic Graph Representation for Biometric Recognition With Occlusions.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,45(12),15120-15136.
MLA Ren, Min,et al."Multiscale Dynamic Graph Representation for Biometric Recognition With Occlusions".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 45.12(2023):15120-15136.
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