An Approach for Face Recognition Based on Fusion of DTCWT and Manifold Regularized Orthogonal Discriminant Analysis

摘要：

In this paper, a novel subspace learning method called Manifold Regularized Orthogonal Discriminant Analysis (MRODA) is first proposed. Based on within-class local geometry preservation and Least Square regression framework for LDA, MRODA can encode both the local geometry and discriminant structures of face data manifolds, and can address the small sample size problem through pseudo-inverse resolution. The transform vectors are orthogonalized to improve their discriminatory performance. Based on the selected Dual-Tree Complex Wavelet Transform features, an approach for face recognition based on the fusion of spatial and frequency features is developed. Experimental results on ORL, Yale and AR face databases show the effectiveness of the proposed approach.

关键词： Face Recognition Manifold Regularized Orthogonal Discriminant Analysis Dual-Tree Complez Wavelet Feature Selection Information Fusion

作者: Qiang Zhang Yunze Cai Xiaoming Xu

作者单位: School of Electric and Information Engineering, Shanghai Jiaotong University , Shanghai 200240 School of Electric and Information Engineering, Shanghai Jiaotong University , Shanghai 200240 Unive

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 2409-2414

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）