摘要
多生物特征的融合与识别可提高身份识别系统的整体性能.本文在研究特征层融合的基础上,结合二维Fisher线性判别分析(2-Dimensional Fisher Linear Discriminant Analysis,2DFLD),提出了一种人脸与虹膜特征融合与识别模型.首先,对人脸图像与虹膜图像分别进行压缩降维处理,得到相应的初始特征矩阵.然后将人脸与虹膜的初始特征矩阵进行组合,获得组合特征矩阵.同时,利用2DFLD算法对组合特征矩阵进行融合,获得了人脸与虹膜的融合特征.最后运用最小距离分类器进行识别.基于ORL(Olivetti Research Laboratory)人脸数据库和CASIA(Chinese Academy ofSciences,Institute of Automation)虹膜数据库的实验结果表明,该模型实现了特征层融合,不仅克服了"小样本"效应,而且有效提高了身份识别的正确识别率,为多生物特征身份识别提供了一种有效模型.
Multimodal biometric fusion and identification technique can improve the performance of identification system. Combined with 2-Dimensional Fisher Linear Discriminant Analysis （2DFLD） ,a model for face and iris feature fusion and recognition is presented in this paper. Firstly, compression is done to face and iris image respectively, and the two corresponding original feature matrixes are obtained. Secondly, the two original feature matrixes from face and iris image are integrated into one matrix, and a combined feature matrix is formed. Then feature extraction to the combined feature matrix is done by 2DFLD, and a fused feature matrix is constructed. Finally, Nearest Neighbor Decision （NND） nile is used in recognition. Experimental results on ORL （Olivetti Research Laboratory） face database and CASIA （Chinese Academy of Sciences,Institute of Automation） iris database show not only small sample effects can be solved,but also a high correct recognition rate can be gained, and demonstrate that the valid model is supplied for multimodal biometric identification.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2007年第7期1365-1371,共7页
Acta Electronica Sinica
基金
广东省自然科学基金(No.032356)
北京大学视觉与听觉信息处理国家重点实验室开放课题基金(No.0505)
广东省江门市科技攻关(No.江财企[2006]59号)
关键词
二维Fishe
线性判别分析
特征融合
多生物特征识别
人脸识别
虹膜识别
2-dimensional fisher linear discriminant analysis
feature fusion
multimodal biometric recognition
face recognition
iris recognition
