Improving hand vein recognition by score weighted fusion of wavelet-domain multi-radius local binary patterns

Improving hand vein recognition by score weighted fusion of wavelet-domain multi-radius local binary patterns
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通过小波域多半径局部二进制模式的得分加权融合改进手静脉识别

DOI:
10.1504/ijcat.2016.10000485
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发表时间:
2016
影响因子:
1.1
通讯作者:
Huang, Di
Huang, Di
中科院分区:
--
文献类型:
--
作者:
Wang, Yiding;Duan, Qiangyu;Shark, Lik-Kwan;Huang, Di

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在生物识别模式中,由于高不可穿透性和良好的用户便利性,手静脉图案被视为为高级别安全访问应用提供有吸引力的方法。对于基于近红外手背静脉图像的生物特征识别,局部二值模式LBP已成为一种高效的局部图像纹理描述符,具有较高的识别性能。本文将传统的LBP算法应用于空间域,将其扩展到小波域的多半径LBP算法,以提供更全面的特征类别来捕捉静脉模式的灰度变化特征,并提出基于各特征类别相对鉴别能力的评分加权融合算法,以获得更高的识别性能。所提出的方法被证明是提供一个更强大的性能,识别率超过99%,等错误率显着低于2%。
Among biometric modalities, hand vein patterns are seen as providing an attractive method for high-level security access applications owing to high impenetrability and good user convenience. For biometric recognition based on near-infrared dorsal hand vein images, Local Binary Patterns LBP have emerged as a highly effective descriptor of local image texture with high recognition performance reported. In this paper, the traditional approach with LBP applied in the spatial domain is extended to multi-radius LBP in the wavelet domain to provide a more comprehensive set of feature categories to capture grey-level variation characteristics of vein patterns, and score weighted fusion based on the relative discriminative power of each feature category is proposed to achieve higher recognition performance. The proposed methodology is shown to provide a more robust performance with a recognition rate in excess of 99% and an equal error rate significantly less than 2%.
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