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.
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
.
DOI: 10.1007/s12204-013-1417-y
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