Two dimension Double PCA for Extracting Features and Application Based on Between-class Scatter Matrix

摘要：

Conventional PCA usually uses total scatter matrix as a generation matrix, and two dimension image matrices must be transformed into vectors. In this paper, the between-class matrix generated by original image and its eigenvectors were used to feature extracting. First we compressed the image in horizon direction using 2DPCA, then we compressed the feature matrix in vertical direction. Thus, the dimension of features is lesser and the speed of classification is faster. At the same time the category information is fully used and the recognition rate are improved.

关键词： Principal component analysis Feature extraction Face recognition

作者: ZHANG Ruiping LI Dongsheng

作者单位: Department of Electrical Information Taiyuan University of Science and Technology Taiyuan, 030024 Ch Automation Company Taiyuan Iron & Steel (Group) Co., Ltd. Taiyuan, 030003, China

会议类型: 国际会议

会议名称: 2010 International Conference on Smeiconductor Laser and Photonics(2010年半导体激光与光子学国际会议 ICSLP 2010)

会议地点: 成都

会议语种:英文

页码: 56-58

在线出版日期: 2010-10-25（万方平台首次上网日期，不代表论文的发表时间）