Supervised Discriminant Projection for Feature Extraction

摘要：

Unsupervised discriminant projection (UDP) finds an embedding subspace that preserves local structure information, and uncovers and separates embedding corresponding to different manifolds. Though UDP has been applied in many fields, it has limits to solve the classification tasks, such as the ignorance of the class information. Thus, a novel subspace method, called supervised discriminant projection (SDP), is proposed for face recognition in this paper. In our method, the class information was utilized in the procedure of feature extraction. In SDP, the local structure of the original data is constructed according to a certain kind of similarity between data points, which takes special consideration of both the local information and class information. Experiments conducted on two popular face image databases (Yale and AR) demonstrate the effectiveness of the proposed method.

关键词： unsupervised discriminant projection (UDP) supervised discriminant projection (SDP) feature extraction face recognition

作者: Jianguo Wang Jizhao Hua Changying Zhou

作者单位: Department of Computer Science & Technology Tangshan College Tangshan, China College of Information Engineer Yangzhou University Yangzhou, China

会议类型: 国际会议

会议名称: 2011 3rd International Conference on Computer and Automation Engineering(ICCAE 2011)(2011年第三届IEEE计算机与自动化工程国际会议)

会议地点: 重庆

会议语种:英文

页码: 200-203

在线出版日期: 2011-01-21（万方平台首次上网日期，不代表论文的发表时间）