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Biometric verification using thermal images of palm-dorsa vein patterns

机译:使用Palm-Dorsa静脉图案的热图像进行生物识别验证

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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:本文介绍了使用Palm-Dorsa静脉图案的热图像进行个人验证的新方法。所提出的方法的特征是,不需要关于对象的先验知识是必要的,并且可以自动设置参数。在我们的工作中,采用红外线(IR)相机作为输入设备以捕获Palm-Dorsa的热图像。在所提出的方法中,两个手指网被自动选择为基准点以定义热图像上的感兴趣区域(ROI)。在每个ROI内,通过基于热图像的性质修改流域变换的基本工具来提取静脉图案(FPVPS)的特征点。根据导热法(傅立叶定律),可以从每个FPVP提取多种特征以进行验证。使用FPVPS的图像的多分辨率表示使用多个多分辨率滤波器(MRF)来通过滤除每个FPVP的杂种特征来提取主点。然后应用分层集成函数来集成多个特征和多分辨率表示。前者通过帧间个人变异率集成,后者通过正布尔函数集成。我们还介绍了一种逻辑和合理的方法来选择训练阈值以进行验证。使用棕榈背渣的热图像进行实验,结果令人满意,精度可接受(FRR:2.3%,远:2.3%)。实验结果表明,我们所提出的方法对静脉模式验证有效和有效。

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