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A Code-Level Approach to Heterogeneous Iris Recognition
Liu, Nianfeng; Liu, Jing; Sun, Zhenan; Tan, Tieniu
Source PublicationIEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
2017-10-01
Volume12Issue:10Pages:2373-2386
SubtypeArticle
AbstractMatching heterogeneous iris images in less constrained applications of iris biometrics is becoming a challenging task. The existing solutions try to reduce the difference between heterogeneous iris images in pixel intensities or filtered features. In contrast, this paper proposes a code-level approach in heterogeneous iris recognition. The non-linear relationship between binary feature codes of heterogeneous iris images is modeled by an adapted Markov network. This model transforms the number of iris templates in the probe into a homogenous iris template corresponding to the gallery sample. In addition, a weight map on the reliability of binary codes in the iris template can be derived from the model. The learnt iris template and weight map are jointly used in building a robust iris matcher against the variations of imaging sensors, capturing distance, and subject conditions. Extensive experimental results of matching cross-sensor, high-resolution versus low-resolution and, clear versus blurred iris images demonstrate the code-level approach can achieve the highest accuracy in compared with the existing pixel-level, feature-level, and score-level solutions.
KeywordIris Recognition Heterogeneous Cross-sensor Markov
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TIFS.2017.2686013
WOS KeywordSUPERRESOLUTION ; SYSTEMS
Indexed BySCI
Language英语
Funding OrganizationNational Key Research and Development Program of China(2016YFB1001000) ; Strategic Priority Research Program of the Chinese Academy of Sciences(XDB02080007) ; National Natural Science Foundation of China(61573360)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000406238100001
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14807
Collection智能感知与计算研究中心
AffiliationChinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techn, Natl Lab Pattern Recognit,Ctr Res Intelligent Per, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Liu, Nianfeng,Liu, Jing,Sun, Zhenan,et al. A Code-Level Approach to Heterogeneous Iris Recognition[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2017,12(10):2373-2386.
APA Liu, Nianfeng,Liu, Jing,Sun, Zhenan,&Tan, Tieniu.(2017).A Code-Level Approach to Heterogeneous Iris Recognition.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,12(10),2373-2386.
MLA Liu, Nianfeng,et al."A Code-Level Approach to Heterogeneous Iris Recognition".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 12.10(2017):2373-2386.
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