Abstract
Recently, local discriminant embedding (LDE) was proposed as a means of addressing manifold learning and pattern classification. In the LDE framework, the neighbor and class of data points are used to construct the graph embedding for classification problems. From a high dimensional to a low dimensional subspace, data points of the same class maintain their intrinsic neighbor relations, whereas neighboring data points of different classes no longer stick to one another. But, neighboring data points of different classes are not deemphasized efficiently by LDE and it may degrade the performance of classification. In this paper, we investigate its extension, called class mean embedding (CME), using class mean of data points to enhance its discriminant power in their mapping into a low dimensional space. After joined class mean data points, (1) CME may cause each class of data points to be more compact in the high dimension space; (2) CME may increase the quantity of data points, and solves the small sample size (SSS) problem; (3) CME may preserve well the local geometry of the data manifolds in the embedding space. Experimental results on ORL, Yale, AR, and FERET face databases show the effectiveness of the proposed method.
Similar content being viewed by others
References
Belhumeur PN, Hespanha JP, Kriengman DJ (1997) Eigenfaces vs Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7): 711–720
Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6): 1373–1396
Chen H, Chang H, Liu T (2005) Local discriminant embedding and itsvariants. CVPR
He X, Niyogi P (2003) Locality preserving projections. In: Proc. 16th Conference neural information processing systems
Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290: 2323–2326
Tenenbaum JB, de Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290: 2319–2323
Turk M, Pentland AP (1991) Face recognition using eigenfaces. In: IEEE conference on computer vision and pattern recognition, pp 586–591
Yan S, Xu D, Zhang B, Zhang H, Yang Q, Lin S (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans on Pattern Anal and Mach Intell (T-PAMI) pp 40–51
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wan, M., Yang, G., Huang, W. et al. Class mean embedding for face recognition. Artif Intell Rev 36, 285–297 (2011). https://doi.org/10.1007/s10462-011-9214-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10462-011-9214-1