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

doi:10.1007/s12204-013-1417-y

Abstract

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.

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.

CLC Number:

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]. Journal of shanghai Jiaotong University (Science), 2013, 18(4): 425-433.

References

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