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首页> 外文期刊>International Journal of Innovative Computing Information and Control >DEEP BELIEF NETWORK BASED FINGER VEIN RECOGNITION USING HISTOGRAMS OF UNIFORM LOCAL BINARY PATTERNS OF CURVATURE GRAY IMAGES
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DEEP BELIEF NETWORK BASED FINGER VEIN RECOGNITION USING HISTOGRAMS OF UNIFORM LOCAL BINARY PATTERNS OF CURVATURE GRAY IMAGES

机译：基于深度信仰网络的手指静脉识别使用曲率灰色图像均匀局部二元图案的直方图

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摘要

In the existing biometric recognition area, the Finger Vein Recognition (FVR) technology has strong universality and applicability. Conventional FVR schemes are based on extracting features of the overall image or features of the vein pattern. Because there are a large amount of redundant data in features acquired from the whole Finger Vein Image (FVI), the time complexity is high, and the features extracted from the vein pattern are greatly affected by the image segmentation algorithm. Aiming at the low recognition rate of traditional FVR technology and the high requirement of image preprocessing in the early stage, this paper proposes a finger vein recognition method based on Deep Learning (DL) and features extracted from curvature gray images. Firstly, the curvature of the finger vein image is calculated by using a two-dimensional Gaussian template, and the curvature gray image is obtained. And then the histogram feature is generated by the Uniform Local Binary Pattern (ULBP) operator, and input into the Deep Belief Network (DBN) for vein image recognition. Experimental results show that our scheme is effective and better than existing schemes.
机译：在现有的生物识别区域中，手指静脉识别（FVR）技术具有强大的普遍性和适用性。传统的FVR方案基于提取静脉图案的整体图像或特征的提取特征。因为从整个手指静脉图像（FVI）获取的特征中存在大量冗余数据，所以时间复杂度高，并且从静脉模式提取的特征受到图像分割算法的大大影响。目的，瞄准传统FVR技术的低识别率和早期图像预处理的高要求，本文提出了一种基于深度学习（DL）的手指静脉识别方法和从曲率灰度图像提取的特征。首先，通过使用二维高斯模板来计算指静脉图像的曲率，并且获得曲率灰度图像。然后，直方图特征由均匀的本地二进制模式（ULBP）操作者生成，并输入到深度信仰网络（DBN）中，用于静脉图像识别。实验结果表明，我们的计划是有效的，比现有计划更好。

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