基于牙印图像几何特征的识别技术

IF 3.2 Q2 AUTOMATION & CONTROL SYSTEMS Systems Science & Control Engineering Pub Date : 2022-09-12 DOI:10.1080/21642583.2022.2119440
Ning Wang, Jiafa Mao, Lixin Wang, Yahong Hu
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引用次数: 0

摘要

身份识别技术是一种基于某种生物特征实现身份验证的技术。进入互联网时代后,该技术成为计算机领域的热门研究方向。本文将牙印图像作为生物特征进行识别算法的研究。本文采用基于神经网络的目标检测算法对单个牙印区域的目标进行检测,构建目标检测网络。实验结果表明,该方法对目标区域具有良好的分割效果,分割准确率达到91.66%。根据采集到的牙纹图像的轮廓特征,设计了一套牙孔面积比特征提取方法。为了客观地评价识别和分类方法,使用支持向量机作为最终分类器。识别准确率为94.09%,验证准确率为94.09%。测试准确率为91.46%,分类效果优异。本文在前人牙印模型研究的基础上取得了很大的突破和明显的进展。
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Identification technology based on geometric features of tooth print images
Identity recognition technology is a type of technology that realizes identity verification based on certain biological characteristics. After entering the Internet era, this technology has become a popular research direction in the computer field. In this paper, the image of the tooth print is used as the biological feature to carry out the research on the identification algorithm. This paper adopts the target detection algorithm based on neural network to detect a single tooth imprint area of the target, build a target detection network. The experimental results show that the method has a good segmentation effect on the target area, and the accuracy rate is 91.66%. According to the contour features of the collected tooth print images, a set of tooth pore area ratio feature extraction methods are designed. To objectively evaluate the recognition and classification method, the support vector machine is used as the final classifier. The recognition accuracy rate is 94.09%, and the verification accuracy rate is 94.09%. The test accuracy rate is 91.46%, and the classification effect is excellent. This paper has made a lot of breakthroughs and obvious progress based on the previous research on the tooth impression model.
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来源期刊
Systems Science & Control Engineering
Systems Science & Control Engineering AUTOMATION & CONTROL SYSTEMS-
CiteScore
9.50
自引率
2.40%
发文量
70
审稿时长
29 weeks
期刊介绍: Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory
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