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