多手工方法融合的腕部静脉识别

2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) Pub Date : 2021-06-11 DOI:10.1109/HORA52670.2021.9461367

F. O. Babalola, Önsen Toygar, Y. Bi̇ti̇ri̇m

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引用次数: 2

摘要

本文提出了一种腕部静脉识别系统。该系统结合了三种基于纹理的特征描述符，即二值化统计图像特征多滤波器(M-BSIF)、二维Gabor滤波器和Decision-Level Fusion梯度方向直方图。该方法在FYO和PUT数据库中获得的两个公开可用数据集上进行了测试。该方法优于单个描述符，在FYO和PUT数据库上分别达到95.63%和93.92%的准确率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Wrist Vein Recognition by Fusion of Multiple Handcrafted Methods

A wrist vein recognition system is proposed in this paper. The system combines three texture-based feature descriptors, namely, multiple filters of Binarized Statistical Image Features (M-BSIF), 2D Gabor filter, and Histogram of Gradient orientation by Decision-Level Fusion. The method was tested on two publicly available datasets obtained from FYO and PUT databases. The proposed method outperforms the individual descriptors and achieves 95.63% and 93.92% accuracies on FYO and PUT databases, respectively.

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来源期刊

2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)

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