Semi-supervised Bi-directional Dimensionality Reduction for Face Recognition

摘要：

A novel method for face recognition called semisupervised bi-directional dimensionality reduction (SSBDR) is proposed. Based on semi-supervised learning, domain knowledge in the form of pairwise constraints besides abundant unlabeled examples are available, which specifies whether a pair of instances belong to the same class or not. Compared to the semi-supervised dimensionality reduction (SSDR), it can not only preserve the intrinsic structure of the unlabeled data as well as both the must-link (the same class) and cannot-link constraints (different classes) deflned on the labeled examples in the projected low-dimensional space, but also constructs two image covariance matrices directly by the original image matrix in two directions which can reduce the dimension of the original image matrix in two directions. The validity of this method can be verified by the experiments on ORL face database.

关键词： Face recognition Semi-supervised learning Pattern classi cation Principal component analysis Dimension reduction

作者: Lihua Wang Chunjian Ren Hongbo Xu Chanchan Qin

作者单位: College of Physical Science & Technology Huazhong Normal University Wuhan,P.R.China Computer Science & Technology Donghua University Shanghai,P.R.China

会议类型: 国际会议

会议名称: 2010 IEEE信息与自动化国际会议(ICIA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 1-4

在线出版日期: 2010-06-20（万方平台首次上网日期，不代表论文的发表时间）