The Analysis of Parameters t and k of LPP on Several Famous Face Databases

The subspace transformation plays an important role in the face recognition. LPP, which is so-called the Laplacianfaces, is a very popular manifold subspace transformation for face recognition, and it aims to preserve the local structure of the samples. Recently, many variants of LPP are proposed. LPP is a baseline in their experiments. LPP uses the adjacent graph to preserve the local structure of the samples. In the original version of LPP, the local structure is determined by the parameters t (the heat kernel) and k (k-nearest neighbors) and directly influences on the performance of LPP. To the best of our knowledge, there is no report on the relation between the performance and these two parameters. The objective of this paper is to reveal this relation on several famous face databases, i.e. ORL, Yale and YaleB.