Orthogonal Laplacianfaces for Face Recognition

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Following the intuition that the naturally occurring face data
may be generated by sampling a probability distribution that has support
on or near a submanifold of ambient space, we propose an appearancebased face recognition method, called orthogonal Laplacianface. Our algorithm is based on the locality preserving projection (LPP) algorithm, which
aims at finding a linear approximation to the eigenfunctions of the Laplace
Beltrami operator on the face manifold. However, LPP is nonorthogonal,
and this makes it difficult to reconstruct the data. The orthogonal locality
preserving projection (OLPP) method produces orthogonal basis functions
and can have more locality preserving power than LPP. Since the locality
preserving power is potentially related to the discriminating power, the
OLPP is expected to have more discriminating power than LPP. Experimental results on three face databases demonstrate the effectiveness of our
proposed algorithm.