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A new method for automatic extraction of region of interest from infrared images of dorsal hand vein pattern based on floating selection model

机译:基于浮动选择模型的手背静脉红外图像自动提取感兴趣区域的新方法

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摘要

Personal identification based on vein pattern is one of the latest biometric approaches that have ever attracted lots of attentions. The method of personal identification suggested in this study utilises the individual's dorsal hand vein pattern. However, hand spin and relocation in different trials of image acquisition is a limiting factor in application of this approach. We introduce a new procedure for automatic selection of region of interest (ROI) designated 'floating ROI' in which adjusting the lengths and angles of sides in the ROI quadrant, the imaging process stays resistant against any hand relocation. Moreover, a new method for the vein pattern extraction called 'square thresholding' is introduced that greatly improves the extraction of vein-patterns. For this, the average of grey level of the pixels in a 5 × 5 neighbourhood is compared with 9 × 9 neighbourhood for any pixel. To verify validity of the proposed methods, 1,200 images taken from 100 individuals is used. As a result, an identification rate with the accuracy of 96.41% is obtained.
机译:基于静脉模式的个人识别是最新的生物识别方法之一,吸引了众多关注。在这项研究中建议的个人识别方法利用个人的背手静脉模式。但是,在不同的图像采集试验中,手旋转和重定位是该方法应用的限制因素。我们引入了一种自动选择感兴趣区域(ROI)的新程序,称为“浮动ROI”,其中调整了ROI象限中边的长度和角度,成像过程可以抵抗手的任何重定位。此外,介绍了一种称为“平方阈值”的用于静脉图案提取的新方法,该方法极大地改善了静脉图案的提取。为此,对于任何像素,将5×5邻域中像素的平均灰度与9×9邻域进行比较。为了验证所提出方法的有效性,使用了100个人拍摄的1,200张图像。结果,获得了准确率为96.41%的识别率。

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