Algorithm study of face recognition on improved 2DLDA

摘要：

Linear Discriminant Analysis (LDA) is a well-known method for face recognition in feature extraction and dimension reduction. To solve the small sample effect of LDA, Two-Dimensional Linear Discriminant Analysis (2DLDA) has been used for face recognition recently, but its could hardly take use of the relationship between the adjacent scatter matrix. In this paper, I improved the between-class scatter matrix, proposed paired-class scatter matrix for face representation and recognition. In this new method, a paired between-class scatter matrix distance metric is used to measure the distance between random paired between-class scatter matrix. To test this new method, ORL face database is used and the results show that the paired between-class scatter matrix based 2DLDA method (N2DLDA) outperforms the 2DLDA method and achieves higher classification accuracy than the 2DLDA algorithm.

关键词： Face recognition Scatter matrix Feature extraction 2DLDA

作者: Li Shiping Cheng Yu Liu Huibin Mu Lin

作者单位: College of Information Science and Engineering Northeastern University Shenyang,China College of Information Science and Engineering Northeastern University ,Shenyang,China

会议类型: 国际会议

会议名称: 4th International Conference on Measuring Technology and Mechatronics Automation(第四届检测技术与机电自动化国际会议 ICMTMA 2012)

会议地点: 三亚

会议语种:英文

页码: 58-61

在线出版日期: 2012-01-06（万方平台首次上网日期，不代表论文的发表时间）