Image Reconstruction for Face Recognition Based on 2DPCA and SVM

摘要：

In contrast to the covariance matrix of principal component analysis (PCA), two-dimensional PCA (2DPCA) is easier to evaluate the covariance matrix accurately and has less time to determine the corresponding eigenvectors. Based on the high performance of 2DPCA in dimensional reduction and SVM in tackling small sample size, high dimension and its good generalization, a new method of face recognition based on 2DPCA+SVM is proposed in this paper. The computer simulation illustrates the effectivity of this method on the ORL database.

作者: Changjun Zhou Xiaopeng Wei Qiang Zhang Boxiang Xiao

作者单位: School of Mechanical Engineering, Dalian University of Technology Dalian , 116024, P.R.China Liaoning Key Lab of Intelligent Information Processing Dalian University, Dalian, 116622, P.R.China

会议类型: 国际会议

会议名称: Fourth International Conference on Impulsive and Hybrid Dynamical Systems(ICIHDS 2007)(第四届国际脉冲和混合动力系统学术会议)

会议地点: 南宁

会议语种:英文

在线出版日期: 2007-07-20（万方平台首次上网日期，不代表论文的发表时间）