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Biometric verification using thermal images of palm-dorsa vein patterns

机译:使用掌背静脉图案的热图像进行生物特征验证

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A novel approach to personal verification using the thermal images of palm-dorsa vein patterns is presented in this paper. The characteristics of the proposed method are that no prior knowledge about the objects is necessary and the parameters can be set automatically. In our work, an infrared (IR) camera is adopted as the input device to capture the thermal images of the palm-dorsa. In the proposed approach, two of the finger webs are automatically selected as the datum points to define the region of interest (ROI) on the thermal images. Within each ROI, feature points of the vein patterns (FPVPs) are extracted by modifying the basic tool of watershed transformation based on the properties of thermal images. According to the heat conduction law (the Fourier law), multiple features can be extracted from each FPVP for verification. Multiresolution representations of images with FPVPs are obtained using multiple multiresolution filters (MRFs) that extract the dominant points by filtering miscellaneous features for each FPVP. A hierarchical integrating function is then applied to integrate multiple features and multiresolution representations. The former is integrated by an inter-to-intra personal variation ratio and the latter is integrated by a positive Boolean function. We also introduce a logical and reasonable method to select a trained threshold for verification. Experiments were conducted using the thermal images of palm-dorsas and the results are satisfactory with an acceptable accuracy rate (FRR:2.3% and FAR:2.3%). The experimental results demonstrate that our proposed approach is valid and effective for vein-pattern verification.
机译:本文提出了一种使用手掌背静脉图案的热图像进行个人验证的新颖方法。所提出的方法的特征在于不需要关于对象的先验知识并且可以自动设置参数。在我们的工作中,采用红外(IR)摄像机作为输入设备来捕获掌背的热图像。在提出的方法中,自动选择两个手指腹板作为基准点,以定义热图像上的关注区域(ROI)。在每个ROI中,通过根据热图像的属性修改分水岭变换的基本工具,提取静脉图案(FPVP)的特征点。根据热传导定律(傅里叶定律),可以从每个FPVP中提取多个特征进行验证。使用多个多分辨率滤镜(MRF)获得具有FPVP的图像的多分辨率表示,该滤镜通过过滤每个FPVP的杂项特征来提取优势点。然后应用层次积分功能来积分多个特征和多分辨率表示。前者通过内部到内部个人变异率进行积分,而后者通过正布尔函数进行积分。我们还介绍了一种逻辑合理的方法来选择经过训练的阈值进行验证。使用棕榈背的热图像进行实验,结果令人满意,准确率可接受(FRR:2.3%和FAR:2.3%)。实验结果表明,我们提出的方法是有效和有效的静脉模式验证。

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