摘要
针对掌纹采集受外界因素和噪声的影响较大,传统方法掌纹识别率低、鲁棒性差等问题,提出一种基于特征加权与核主成分分析的掌纹识别算法.首先采用Curvelet变换对掌纹图像进行分解,得到不同尺度和角度的轮廓系数,并对Curvelet系数进行加权融合操作;然后通过核主成分分析对掌纹特征进行降维处理,实现特征提取;最后采用相关向量机实现掌纹匹配,并采用PolyU掌纹图像对算法的性能进行测试.结果表明,与其他掌纹识别算法相比,该算法取得的掌纹识别率更高,且掌纹匹配的时间最短,可以满足掌纹实时识别要求.
Aiming at the problems that palmprint acquisition was influenced by external factors and noise, palmprint recognition rate of traditional method was low and robustness was poor, we proposed a new palmprint recognition algorithm based on feature weighted and kernel principal component analysis. Firstly, palmprint image was decomposed by Curvelet transform, contour coefficients of different scales and angles were obtained, and Curvelet coefficients were weighted by the fusion operation. Secondly, feature extraction was realized by using kernel principal component analysis to reduce dimension of palmprint feature. Finally, relevance vector machine was used to realize palmprint matching, and the performance of the algorithm was tested by using PolyU palmprint image. The test results show that, compared with other palmprint recognition algorithms, the proposed algorithm has higher palmprint recognition rate, and palmprint matching time is the shortest, which can meet the requirements of real-time palmprint recognition.
出处
《吉林大学学报（理学版）》
CAS
CSCD
北大核心
2016年第6期1361-1366,共6页
Journal of Jilin University:Science Edition
基金
国家自然科学基金(批准号:11401284)
高校博士学科点专项科研联合基金(批准号:20132121110009)
关键词
掌纹识别
特征降维
CURVELET变换
相关向量机
核主成分分析
palmprint recognition
feature dimension reduction
Curvelet transform
relevance vector maehine
kernel principal component analysis
作者简介
高雷阜（1963-），男，汉族，博士，教授，博士生导师，从事最优化理论与方法、控制与决策的研究，E—mail：Sammy297@foxmail.com．
通信作者：李超（1984-），男，汉族，博士研究生，从事数字图像处理和管理信息系统的研究，E-mail：lichao19840614@foxmail.com．
