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

doi:10.1007/s12204-013-1417-y

上海交通大学学报（英文版） ›› 2013, Vol. 18 ›› Issue (4): 425-433.doi: 10.1007/s12204-013-1417-y

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

ZHANG Qiang1* (张强), CAI Yun-ze1 (蔡云泽), XU Xiao-ming1,2,3 (许晓鸣)

(1. School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China; 2. University of Shanghai for Science and Technology, Shanghai 200093, China; 3. Shanghai Academy of Systems Science, Shanghai 200093, China)

出版日期:2013-08-28 发布日期:2013-08-12
通讯作者: ZHANG Qiang (张强) E-mail:zhangqiang741002@sina.com

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

ZHANG Qiang1* (张强), CAI Yun-ze1 (蔡云泽), XU Xiao-ming1,2,3 (许晓鸣)

(1. School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, China; 2. University of Shanghai for Science and Technology, Shanghai 200093, China; 3. Shanghai Academy of Systems Science, Shanghai 200093, China)

Online:2013-08-28 Published:2013-08-12
Contact: ZHANG Qiang (张强) E-mail:zhangqiang741002@sina.com

摘要/Abstract

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.

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

Abstract: 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.

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

中图分类号:

TP 391

ZHANG Qiang1* (张强), CAI Yun-ze1 (蔡云泽), XU Xiao-ming1,2,3 (许晓鸣). Orthogonal Discriminant Improved Local Tangent Space Alignment Based Feature Fusion for Face Recognition[J]. 上海交通大学学报（英文版）, 2013, 18(4): 425-433.

ZHANG Qiang1* (张强), CAI Yun-ze1 (蔡云泽), XU Xiao-ming1,2,3 (许晓鸣). Orthogonal Discriminant Improved Local Tangent Space Alignment Based Feature Fusion for Face Recognition[J]. Journal of shanghai Jiaotong University (Science), 2013, 18(4): 425-433.

参考文献

[1] Turk M A, Pentland A P. Face recognition using eigenfaces [C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Maui, Hawaii, USA: IEEE, 1991: 586-591.
[2] Belhumeur P N, Hespanha J P, Kriegman D J. Eigenfaces vs. fisherfaces: Recognition using classspecific linear projection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720.
[3] He X, Cai D, Yan S, et al. Neighborhood preserving embedding [C]//Proceedings of the 10th IEEE International Conference on Computer Vision. Beijing, China: IEEE, 2005: 1208-1213.
[4] He X F, Yan S C, Hu Y X, et al. Face recognition using Laplacian faces [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340.
[5] Zhang T, Yang J, Zhao D, et al. Linear local tangent space alignment and application to face recognition [J]. Neurocomputing, 2007, 70: 1547-1553.
[6] Hu H F. Orthogonal neighborhood preserving discriminant analysis for face recognition [J]. Pattern Recognition, 2008, 41(6): 2045-2054.
[7] Li B, Huang D S, Wang C, et al. Feature extraction using constrained maximum variance mapping [J]. Pattern Recognition, 2008, 41(11): 3287-3294.
[8] Li B, Zheng C, Huang D. Locally linear discriminant embedding: An efficient method for face recognition [J]. Pattern Recognition, 2008, 41(12): 3813-3821.
[9] Li H F, Jiang T, Zhang K S. Efficient and robust feature extraction by maximum margin criterion [J]. IEEE Transactions on Neural Networks, 2006, 17(1): 157-165.
[10] Saul L K, Roweis S T. Think globally, fit locally: Unsupervised learning of low dimensional manifolds [J]. Journal of Machine Learning Research, 2003, 4(2): 119-155.
[11] Lei Y K, Xu Y M, Yang J A, et al. Feature extraction using orthogonal discriminant local tangent space alignment [J]. Pattern Analysis & Applications, 2012, 15(3): 249-259.
[12] Zhang P, Qiao H, Zhang B. An improved local tangent space alignment method for manifold learning [J]. Pattern Recognition Letters, 2011, 32(2): 181-189.
[13] Chaudhury K N, Unser M. Construction of Hilbert transform pairs of wavelet bases and Gabor-like transforms [J]. IEEE Transactions on Signal Processing, 2009, 57(9): 3411-3425.
[14] Kingsbury N. The dual-tree complex wavelet transform: A new efficient tool for image restoration and enhancement [C]//Proceedings of the 17th European Signal Processing Conference. Rhodos, Greece: Elsevier, 1998: 319-322.
[15] Yan S, Xu D, Zhang B, et al. Graph embedding and extensions: A general framework for dimensionality reduction [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(1): 40-51.
[16] Cai D, He X, Han J. SRDA: An efficient algorithm for large-scale discriminant analysis [J]. IEEE Transactions on Knowledge and Data Engineering, 2008, 20(1): 1-12.
[17] Martinez A, Benavente R. The AR database [R]. Barcelona, Spain: CVC, 1998.
[18] Sim T, Baker S, Bsat M. The CMU pose, illumination, and expression database [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(12): 1615-1618.

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

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

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