[1]颜廷秦,周昌雄.二维EMD分解提高PCA掌纹识别率[J].智能系统学报,2013,8(4):377-380.[doi:10.3969/j.issn.1673-4785.201211002]
YAN Tingqin,ZHOU Changxiong.The research of improving PCA recognition rate of palmprints with BEMD[J].CAAI Transactions on Intelligent Systems,2013,8(4):377-380.[doi:10.3969/j.issn.1673-4785.201211002]
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YAN Tingqin,ZHOU Changxiong.The research of improving PCA recognition rate of palmprints with BEMD[J].CAAI Transactions on Intelligent Systems,2013,8(4):377-380.[doi:10.3969/j.issn.1673-4785.201211002]
二维EMD分解提高PCA掌纹识别率
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
8
期数:
2013年第4期
页码:
377-380
栏目:
学术论文—机器学习
出版日期:
2013-08-25
- Title:
- The research of improving PCA recognition rate of palmprints with BEMD
- 文章编号:
- 1673-4785(2013)04-0377-04
- Keywords:
- BEMD; IMF; PCA; palmprints; Biometric identification
- 分类号:
- TP391
- 文献标志码:
- A
- 摘要:
- 为了提高常用于在线掌纹识别的PCA方法的识别率，提出融合BEMD技术的PCA掌纹识别方法.二维EMD技术能够在频率域内实现图像的多层分解，在不同频段内对图像进行处理.掌纹图像的低频部分容易受到背景等因素的影响，所以实验中提取、利用掌纹高频信息，去除低频信息，充分利用掌纹中的个人特征信息，抑制干扰，提高识别率.基于香港理工大学掌纹数据库的仿真结果显示，这种方法的识别率远高于传统PCA方法，体现了一定的理论研究意义和实用价值.
- Abstract:
- For improving the recognition rate of principal component analysis(PCA) which often used in palmprint online recognition system, a new palmprint recognition method with PCA and bi_dimensional emperical mode decomposition（BEMD） is proposed in this article. An image can be decomposed with BEMD in frequency domain, so it can be processed in different frequency domains. Because the low frequency part of palmprints sis often influenced by the background, the high frequency information is used in our experiment to highlight the personal characristics, and as the result, the recognition rate is improved and the speed is faster. The result of experiments with the palmprint database of Hong Kong Polytechnic University shows the recognition rate of BEMD and PCA is more higher than traditional PCA, and the results also indicate that this method plays an important role in both theoretical research and practical application.
- 参考文献/References:
-
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备注/Memo
收稿日期：2012-10-26.???? 网络出版日期：2013-03-26.
基金项目：国家自然科学基金资助项目(60970058)；江苏省自然科学基金资助项目(BK2009131)；苏州市科技基础设施建设计划资助项目（SZS201009）；苏州市职业大学创新团队建设资助项目(3100125)； 苏州市职业大学校级课题资助项目（2012SZDYY04）；苏州市云计算智能信息处理高技术研究重点实验室开放基金项目（SXZ201304）.
通信作者：颜廷秦. E-mail：ytqmax@gmail.com.
作者简介：
颜廷秦，男,1971年生，副教授，主要研究方向为人工智能和数字图像处理，发表学术论文15篇，其中被EI检索2篇. 周昌雄，男，1965年生，教授，博士，主要研究方向为图像处理，发表学术论文30篇，其中被EI检索8篇.
更新日期/Last Update:
2013-09-27