Reassembling 2DLDA for face recognition

摘要：

Two dimensional linear discriminant analysis(2DLDA) provides a solution to the small sample size(S3) problem presented to the classical LDA. However, it takes each column of the image matrix, which only contains partial information of the whole human face, as an input vector. In this paper, a novel reassembling 2DLDA (R2DLDA) algorithm is proposed for face recognition. The new reassembling 2D sample, each column of which is consisted of a sub-image of original face image, is introduced. Then 2DLDA is applied for face recognition. Experimental results on the ORL face database show that the proposed R2DLDA algorithm is feasible and has higher recognition rates than original 2DLDA and Alternate-2DLDA algorithms.

作者: Xiaohua Gu Liping Yang Jun Peng

作者单位: Chongqing University of Science & Technology, Chongqing 401331, China Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing Un

会议类型: 国际会议

会议名称: 2011 International Conference on Computer Science and Network Technology(2011计算机科学与网络技术国际会议 ICCSNT 2011)

会议地点: 哈尔滨

会议语种:英文

页码: 1162-1165

在线出版日期: 2011-12-24（万方平台首次上网日期，不代表论文的发表时间）