Title: A Code-Level Approach to Heterogeneous Iris Recognition
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| Authors: Liu, NF; Liu, J; Sun, ZN; Tan, T
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| Author Full Names: Liu, Nianfeng; Liu, Jing; Sun, Zhenan; Tan, Tieniu
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| Source: IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 12 (10):2373-2386; 10.1109/TIFS.2017.2686013 OCT 2017
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| Language: English
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| Abstract: Matching 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.
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| ISSN: 1556-6013
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| eISSN: 1556-6021
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| IDS Number: FB6FY
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| Unique ID: WOS:000406238100001
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