An Adaptive Method based on QRCP Decomposition for Single Sample Problem

摘要：

　　In this paper,an adaptive approximation image reconstruction method based on orthogonal triangular with column pivoting(QRCP)decomposition algorithm is proposed for single sample problem in face recognition.By using QRCP the single sample and its transpose are decomposed to two sets of basis images.Then an adaptive approximation image reconstruction method is proposed to reconstruct two approximation images from the two basis image sets respectively.The single training sample and its two approximation images of each object form a new training set,which can make the fisher linear discriminant analysis(FLDA)be applied to single sample problem in face recognition.The performance of the proposed method is verified on Yale,FERET,and ORL face databases.The experimental results indicate that the proposed method is efficient and outperforms some existing methods which are proposed to overcome the single sample problem.

关键词： Face recognition single sample problem orthogonal triangular decomposition with column pivoting adaptive approximation image reconstruction method

作者: HU Changhui LU Xiaobo DU Yijun

作者单位: School of Automation,Southeast University,Nanjing 210096,China;Key Laboratory of Measurement and Control of Complex System of Engineering,Ministry of Education,Southeast University,Nanjing 210096,China

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 4826-4830

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）