Orthogonal Discriminant Improved Local Tangent Space Alignment Based Feature Fusion for Face Recognition  

作　　者：张强 蔡云泽 许晓鸣 

机构地区：[1]School of Electronic Information and Electrical Engineering,Shanghai Jiaotong University [2]University of Shanghai for Science and Technology [3]Shanghai Academy of Systems Science

出　　处：《Journal of Shanghai Jiaotong university(Science)》2013年第4期425-433,共9页上海交通大学学报（英文版）

基　　金：the National Natural Science Foundation of China(No.61004088);the Key Basic Research Foundation of Shanghai Municipal Science and Technology Commission(No.09JC1408000)

摘　　要：Improved local tangent space alignment (ILTSA) is a recent nonlinear dimensionality reduction method which can efficiently recover the geometrical structure of sparse or non-uniformly distributed data manifold. In this paper, based on combination of modified maximum margin criterion and ILTSA, a novel feature extraction method named orthogonal discriminant improved local tangent space alignment (ODILTSA) is proposed. ODILTSA can preserve local geometry structure and maximize the margin between different classes simultaneously. Based on ODILTSA, a novel face recognition method which combines augmented complex wavelet features and original image features is developed. Experimental results on Yale, AR and PIE face databases demonstrate the effectiveness of ODILTSA and the feature fusion method.Improved local tangent space alignment （ILTSA） is a recent nonlinear dimensionality reduction method which can efficiently recover the geometrical structure of sparse or non-uniformly distributed data manifold. In this paper, based on combination of modified maximum margin criterion and ILTSA, a novel feature extraction method named orthogonal discriminant improved local tangent space alignment （ODILTSA） is proposed. ODILTSA can preserve local geometry structure and maximize the margin between different classes simultaneously. Based on ODILTSA, a novel face recognition method which combines augmented complex wavelet features and original image features is developed. Experimental results on Yale, AR and PIE face databases demonstrate the effectiveness of ODILTSA and the feature fusion method.

关 键 词：manifold learning linear extension orthogonal discriminant improved local tangent space alignment （ODILTSA） augmented Gabor-like complex wavelet transform face recognition information fusion 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]