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Cross-Modality Face Recognition via Heterogeneous Joint Bayesian
Shi, Hailin1; Wang, Xiaobo1; Yi, Dong2; Lei, Zhen3; Zhu, Xiangyu1; Li, Stan Z.1
AbstractIn many face recognition applications, the modalities of face images between the gallery and probe sets are different, which is known as heterogeneous face recognition. How to reduce the feature gap between images from different modalities is a critical issue to develop a highly accurate face recognition algorithm. Recently, joint Bayesian (JB) has demonstrated superior performance on general face recognition compared to traditional discriminant analysis methods like subspace learning. However, the original JB treats the two input samples equally and does not take into account the modality difference between them and may be suboptimal to address the heterogeneous face recognition problem. In this work, we extend the original JB by modeling the gallery and probe images using two different Gaussian distributions to propose a heterogeneous joint Bayesian (HJB) formulation for cross-modality face recognition. The proposed HJB explicitly models the modality difference of image pairs and, therefore, is able to better discriminate the same/different face pairs accurately. Extensive experiments conducted in the case of visible-near-infrared and ID photo versus spot face recognition problems show the superiority of the HJB over previous methods.
KeywordCross Modality Heterogeneous Face Recognition Joint Bayesian (Jb)
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationNational Key Research and Development Plan(2016YFC0801002) ; Chinese National Natural Science Foundation(61473291 ; AuthenMetric RD Funds ; 61572501 ; 61502491 ; 61572536)
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000393813700003
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Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Alibaba Grp, Hangzhou 311121, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Shi, Hailin,Wang, Xiaobo,Yi, Dong,et al. Cross-Modality Face Recognition via Heterogeneous Joint Bayesian[J]. IEEE SIGNAL PROCESSING LETTERS,2017,24(1):81-85.
APA Shi, Hailin,Wang, Xiaobo,Yi, Dong,Lei, Zhen,Zhu, Xiangyu,&Li, Stan Z..(2017).Cross-Modality Face Recognition via Heterogeneous Joint Bayesian.IEEE SIGNAL PROCESSING LETTERS,24(1),81-85.
MLA Shi, Hailin,et al."Cross-Modality Face Recognition via Heterogeneous Joint Bayesian".IEEE SIGNAL PROCESSING LETTERS 24.1(2017):81-85.
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