Discriminant Improved Local Tangent Space Alignment with Adaptively Weighted Complex Wavelet for Face Recognition

摘要：

Improved Local Tangent Space Alignment (ILTSA) is a recent nonlinear dimensionality reduction method but there exists the out-of-sample problem. In this paper, based on linearization and discriminant extension of ILTSA, a novel feature extraction method named Discriminant Improved Local Tangent Space Alignment (DILTSA) is proposed. DILTSA can preserve both local within-class and between-class geometry structures. Motivated by the recent development of sub-pattern face recognition, an adaptively weighted complex wavelet feature extraction method is proposed. Experimental results on ORL and PIE face databases demonstrate the effectiveness of DILTSA and its combination with complex wavelet features.

关键词： manifold learning linear extension discriminant improved local tangent space alignment complex wavelet adaptive weight face recognition

作者: ZHANG Qiang CAI Yun-ze XU Xiao-ming

作者单位: School of Electronic, Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai School of Electronic, Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai

会议类型: 国际会议

会议名称: The 31st Chinese Control Conference(第三十一届中国控制会议)

会议地点: 合肥

会议语种:英文

页码: 3708-3713

在线出版日期: 2012-07-01（万方平台首次上网日期，不代表论文的发表时间）