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Driver Identification Using Finger-vein Patterns With Radon Transform And Neural Network

机译:使用带有Radon变换和神经网络的手指静脉模式识别驾驶员

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A driver identification system using finger-vein technology and an artificial neural network is presented in this paper. The principle of the proposed system is based on the function of near infra-red finger-vein patterns for biometric authentication. Finger-vein patterns are required by transmitting near infra-red through a finger and capturing the image with an infra-red CCD camera. The algorithm of the proposed system consists of a combination of feature extraction using Radon transform and classification using the neural network technique. The Radon transform can concentrate the information of an image in a few high-valued coefficients in the transformed domain. The neural networks are used to develop the training and testing modules. The artificial neural network techniques using radial basis function network and probabilistic neural network are proposed to develop a driver identification system. The experimental results indicated the proposed system performs well for personal identification. The average identification rate of PNN network is over 99.2%. The details of the image processing technique and the characteristic of system are also described in this paper.
机译:提出了一种基于手指静脉技术和人工神经网络的驾驶员识别系统。所提出的系统的原理是基于用于生物识别的近红外手指静脉图案的功能。通过手指传输近红外光并使用红外CCD相机捕获图像,需要指静脉图案。所提出系统的算法由结合使用Radon变换的特征提取和使用神经网络技术的分类组成。 Radon变换可以将图像信息集中在变换域中的几个高值系数中。神经网络用于开发训练和测试模块。提出了利用径向基函数网络和概率神经网络的人工神经网络技术来开发驾驶员识别系统。实验结果表明,该系统在个人识别方面表现良好。 PNN网络的平均识别率超过99.2%。本文还详细介绍了图像处理技术和系统特性。

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