Enhanced and parameterless Locality Preserving Projections for face recognition

In this paper, we address the graph-based linear manifold learning method for object recognition. The proposed method is called enhanced Locality Preserving Projections. The main contribution is a parameterless computation of the affinity matrix that draws on the notion of meaningful and adaptive neighbors. It integrates two interesting properties: (i) being entirely parameter-free and (ii) the mapped data are uncorrelated. The proposed method has been integrated in the framework of three graph-based embedding techniques: Locality Preserving Projections (LPP), Orthogonal Locality Preserving Projections (OLPP), and supervised LPP (SLPP). Recognition tasks on six public face databases show an improvement over the results of LPP, OLPP, and SLPP. The proposed approach could also be applied to other category of objects.