期刊名称：Neural Information Processing: Letters and Reviews
电子版ISSN：1738-2532
出版年度：2007
卷号：11
期号：2
页码：25-31
出版社：Neural Information Processing
摘要：Subspace-based face recognition is one of the most successful methods for face recognition. Eigenfaces,
Fisherfaces, and Laplacianfaces methods, which are based on PCA, LPP and LDA that preserve global, local
and cluster structure information respectively, are three representative methods of subspace-based face
recognition approaches. In this paper, we propose a novel pattern classification namely Preserving Complete
Subspace Structure Projection (PCSSP) for face recognition. First we analyze their contributions of extracting
the discriminating information respectively firstly, and then we construct a 3D parameter space using three
subspace dimensions as axes. We can take advantage of the global, local and cluster structure information
provided by three subspaces through searching over the whole 3D parameter space instead of searching only
in lines or local regions as the standard subspace methods. Finally based on the 3D parameter space, we propose
a framework for PCA, LPP and LDA. The experimental results with the ORL and Yale face databases show that the
proposed algorithm outperforms three standard subspace approaches, and the proposed algorithm can also improve
the computational efficiency without influencing the recognition performance.
关键词：Preserving complete subspace structure projection, face recognition, principal component analysis,
linear discriminant analysis, locality preserving projections, 3D parameter space
