.png){kind=icon}
返回
Using empirical mode decomposition for iris recognition
.png){kind=icon}
PRE
AI
DOI：10.1016/j.csi.2008.09.013.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Iris recognition is known as an inherently reliable technique for human identification. Empirical Mode Decomposition (EMD), an adaptive multi-resolution decomposition technique, appears to be suitable for non-linear, non-stationary data analysis. Based on EMD, a fully data-driven method without using any predetermined filter or wavelet function, an iris recognition scheme is presented by modifying EMD as a low-pass filter to analyze the iris images. To evaluate the efficacy of the proposed approach, three different similarity measures are used. Experimental results show that three metrics have all achieved promising and similar performance. Therefore, the proposed method demonstrates to be feasible for iris recognition and EMD is suitable for feature extraction. (C) 2008 Elsevier B.V. All rights reserved.
Keyword:
Biometrics
Iris recognition
Empirical Mode Decomposition (EMD)
Multi-resolution decomposition
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
C
IF:
3.1
论文数:
2.3K
被引数:
2.0K
机构
引用论文
分享收藏分享收藏Fast diffusion in a room temperature ionic liquid confined in mesoporous carbon限制在介孔碳中的室温离子液体中的快速扩散
PREAI
分享收藏Author response: Disentangling strictly self-serving mutations from win-win mutations in a mutualistic microbial community作者回应：在互惠互利的微生物群落中区分严格利己突变与双赢突变.png)
OAAI
IF0
OAAI分享收藏分享收藏
查看更多内容