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| 论文编号: |
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| 第一作者所在部门: |
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| 中文论文题目: |
A Code-Level Approach to Heterogeneous Iris Recognition
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| 英文论文题目: |
A Code-Level Approach to Heterogeneous Iris Recognition
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| 论文题目英文: |
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| 作者: |
Liu, Nianfeng;Liu, Jing
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| 论文出处: |
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| 刊物名称: |
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
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| 年: |
2017
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| 卷: |
12 |
| 期: |
10 |
| 页: |
2373-2386 |
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| 收录类别: |
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| 影响因子: |
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| 摘要: |
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. |
| 英文摘要: |
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