二维EMD分解提高PCA掌纹识别率  

The research of improving PCA recognition rate of palmprints with BEMD

作　　者：颜廷秦[1,2] 周昌雄[1] 

机构地区：[1]苏州市职业大学电子信息工程系,江苏苏州215104 [2]苏州市职业大学苏州市云计算智能信息处理高技术研究重点实验室,江苏苏州215104

出　　处：《智能系统学报》2013年第4期377-380,共4页CAAI Transactions on Intelligent Systems

基　　金：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)

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.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.

关 键 词：二维经验模态分解 本征模式函数 主元分析 掌纹 生物特征识别 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]