Finger-Vein Recognition Based on the Score Level Moment Invariants Fusion
Finger-Vein Recognition Based on the Score Level Moment Invariants Fusion
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DOI：
10.1109/cise.2009.5366021
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发表时间：
2009-12
期刊：
影响因子：
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通讯作者：
Xiaohua Qian;Shuxu Guo;Xueyan Li;F. Zhong;Xiangxin Shao
中科院分区：
文献类型：
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作者：
Xiaohua Qian;Shuxu Guo;Xueyan Li;F. Zhong;Xiangxin Shao
提出了一种基于分数级不变矩融合的手指静脉识别算法。针对红外静脉图像对比度低、灰度不均匀的特点，采用最大曲率模型提取手指静脉图案。然后，提取7个不变矩，通过欧氏距离进行匹配。为了解决单一特征法和等权值融合策略在手指静脉识别中误识率高的问题，采用加权平均策略对匹配得分进行融合，以等错误率（EER）最小为目标，得到最优权值。最后，利用融合后的匹配分数进行最终决策。实验结果表明，该算法具有较高的识别率，与单特征方法和等权值融合策略相比，EER至少降低了11%。
This paper expresses an algorithm of finger-vein recognition based on the score level moment invariants fusion. With regard the both characteristics of low contrast and intensity inhomogeneity in the infrared vein images; the maximum curvature model is adopted to extract the finger-vein pattern. Then, seven moment invariants are extracted to be matched by the Euclidean distance. In order to solve the problem of high false rate in finger-vein recognition using the single-feature method and the equal weights fusion strategy, the matching scores are fused by the weighted average strategy, and the equal error rate (EER) is minimized to obtain the optimum weights. Finally, the fused matching score is used to make the final decision. Experiment results prove that our algorithm has high performance on recognition rate, which at least reduces 11% in EER compared with the single-feature method and the equal weights fusion strategy.