首页> 外文会议>Amity International Conference on Artificial Intelligence >Analysis of Finger Vein Feature Extraction and Recognition using DA and KNN Methods
【24h】

Analysis of Finger Vein Feature Extraction and Recognition using DA and KNN Methods

机译:基于DA和KNN方法的手指静脉特征提取与识别分析

获取原文

摘要

A steady growth in terms of development alongwith the use of consumer electronics has been noticed which is a consequence of and increased rate of globalization that has raised the living standards. This has arisen a need for high security authentication systems in order to safeguard personal information stored on mobile devices. Since the high complexity and security of existing biometric systems is becoming the prime concern in terms of space and time, automated personal identification using finger-vein biometric is emerging to be a prominent topic in both practical as well as examination applications. In this paper, using MATLAB 2016a, we present discriminant analysis and KNN of machine learning method, as it stands for K- nearest neighbor algorithm are used to verify and calculate the accuracy of the features of the finger vein images. Thus, the accuracy obtained from KNN is 55.84% whereas from discriminant analysis the accuracy obtained is 92.21%. Finally discriminant analysis is more accurate than KNN technique.
机译:伴随着消费电子产品的使用,发展得到了稳定的增长,这是全球化和生活水平提高的结果。为了保护存储在移动设备上的个人信息,需要高安全性认证系统。由于现有生物特征识别系统的高度复杂性和安全性已成为时空方面的主要问题,因此使用手指静脉生物特征识别的自动个人识别已成为实际和检查应用中的重要主题。在本文中,我们使用MATLAB 2016a进行了判别分析和机器学习方法的KNN,因为它代表K最近邻算法用于验证和计算指静脉图像特征的准确性。因此,从KNN获得的准确度为55.84%,而从判别分析获得的准确度为92.21%。最后,判别分析比KNN技术更准确。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号