首页> 外文期刊>International Journal of Applied Pattern Recognition >Dorsal hand-vein images recognition system based on grey level co-occurrence matrix and Tamura features
【24h】

Dorsal hand-vein images recognition system based on grey level co-occurrence matrix and Tamura features

机译:基于灰度共生矩阵和田村特征的手背静脉图像识别系统

获取原文
获取原文并翻译 | 示例
           

摘要

Biometrics is the important area of distinguishing people using their behavioural characteristics. Until now, researcher and exporter increasing interest with vein pattern biometrics. A vein pattern is a massive link of blood vessels under a person's skin. Similar to fingerprints, in scientific sense, the shape of vascular patterns in the same part of the body has proved distinct from each other. The objective of this paper is to analyse vein images and to design and implement dorsal hand-vein recognition system that has the ability to segment vein and recognise each person based on his vein requires the presence of the human operator. The experimental results indicate that the MDC classifier achieves accuracy of 92% in the case of wavelet transform, and GLCM and Tamura features.
机译:生物识别技术是区分人们的行为特征的重要领域。到目前为止,研究人员和出口商对静脉模式生物识别技术的兴趣日益浓厚。静脉模式是一个人的皮肤下血管的巨大联系。从科学意义上讲,类似于指纹,已证明人体同一部位的血管图案形状彼此不同。本文的目的是分析静脉图像并设计和实现背手静脉识别系统,该系统具有分割静脉并根据每个人的静脉识别每个人的能力而需要操作人员在场的能力。实验结果表明,在小波变换以及GLCM和Tamura特征的情况下,MDC分类器可达到92%的精度。

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