FACE RECOGNITION USING PCA ON ENHANCED IMAGE FOR SINGLE TRAINING IMAGES

摘要：

An image enhancement based Principal component analysis (PCA) method is proposed to deal with face recognition with single training image per person. The method combines the original training image is with its reconstructed image using only a few low-frequency Discrete Cosine Transform (DCT) coefficients and then performs PCA on the enhanced training images set. In comparison with the standard eigenface algorithm and recent single training image based extended eigenface algorithms on ORL face database, the proposed method shows an improvement of more than 6％ in recognition accuracy.

关键词： Face recognition principal component analysis (PCA) eigenface discrete cosine transform (DCT)

作者: JIA-ZHONG HE QING-HUAN ZHU MING-HUI DU

作者单位: School of Information Engineering, Shaoguan College, Shaoguan 512005, China;School of Electronic and School of Information Engineering, Shaoguan College, Shaoguan 512005, China School of Electronic and Information Engineering, South China University of Technology, Guangzhou 51

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 3218-3221

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）