Application of Improved ISOMAP Algorithm for Face Recognition

Abstract

Abstract:

Image data is high-dimensional data which make it easily prone to the dimension disaster. The traditional dimensionality reduction methods can not recover the

inherent structure. Manifold learning is a nonlinear dimensionality reduction technique, it aims to find low-dimensional compact representations of high-dimensional observation

data and explore the inherent law and intrinsic dimension of data. In this paper, the feature extraction method-SIFT and the adaptive ISOMAP method are combined and conducted on

the real face image dataset. This paper analyzes and discusses the problem of the effects of the neighborhood parameter and the intrinsic dimension size on the face image

recognition.

Key words: image retrieval, manifold learning, dimensionality reduction, intrinsic dimension

GUO Hai-feng1,2, CHEN Yue-xia1, SUN Zhou-bao2. Application of Improved ISOMAP Algorithm for Face Recognition[J]. Computer and Modernization.

［1］ Jolliffe I. Principal Component Analysis ［M］. 2nd ed. New York: Springer Verlag, 2002. 
［2］ Comon Pierre. Independent component analysis:A new concept? ［J］. Signal Processing, 1994,36(3):287-314.
［3］ Cox T F, Cox M A A. Multidimensional Scaling［M］. 2nd ed. Chapman and Hall/CRC, 2000.
［4］ Tenenbaum J B, Silva V D, Langford J C. A global geometric framework for nonlinear dimensionality reduction［J］. Science, 2000,290(5500):2319-2323.
［5］ Tenenbaum J B. Mapping a manifold of perceptual observations［C］// Advances in Neural Information Processing Systems. 1998,10：682-688.
［6］ Wen Guihua, Jiang Lijun, Wen Jun. Kernel relative transformation with applications to enhancing locally linear embedding［C］// Proceedings of the International Joint

Conference on Neural Networks. 2008:3401-3406
［7］ Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding［J］. Science, 2001,2909(5500):2323-2326.
［8］ Wang Heyong, Zheng Jie, Yao Zhengan, et al. Improved locally linear embedding through new distance computing［C］// Advances in Neural Networks. 2006:1326-1333.
〖LL〗［9］ Chang H, Yeung D. Robust locally linear embedding［J］. Pattern Recognition, 2006,39(6):1053-1065.
［10］Valencia-Aguirre J, lvarez-Mesa A, Daza-Santacoloma G, et al. Automatic choice of the number of nearest neighbors in locally linear embedding［C］// Progress in

Pattern Recognition, Image Analysis, Computer Vision, and Applications. 2009:77-84.
［11］Eftekhari A, Abrishami-Moghaddam H, Babaie-Zadeh M. k/K-Nearest neighborhood criterion for improvement of locally linear embedding［C］// Computer Analysis of Images and

Patterns. 2009:808-815.
［12］Goldberg Y, Ritov Y. LDR-LLE: LLE with low-dimensional neighborhood representation［C］// Advances in Visual Computing. 2008:43-54.
［13］Zhang Shiqing. Enhanced supervised locally linear embedding［J］. Pattern Recognition Letters, 2009,30(13):1208-1218.
［14］Hou Chenqing, Zhang Changhui, Wu Yi, et al. Stable local dimensionality reduction approaches［J］. Pattern Recognition, 2009,42(9):2054-2066.
［15］Belkin M, Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation［J］. Neural Computation, 2003,15(6):1373-1396.
［16］Lowe D G. Object recognition from local scale-invariant features［C］// Proceeding of IEEE International Conference on Computer Vision. 1999,2:1150-1157.
［17］Lowe D G. Distinctive image features from scale-invariant keypoints［J］. International Journal of Computer Vision, 2004,60(2):91-110.

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Application of Improved ISOMAP Algorithm for Face Recognition

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