Kernel Hetero-associative Memory for Robust Face Recognition
Bailing Zhang · 2006
Associative memory models have been studied as efficient paradigm for visual information processing. In this paper we investigate a new hetero-associative memory (HAM) model based on the Kernel Partial Least Square (KPLS) regression recently proposed in the literature. Steming from the Partial Least Square (PLS) regression, a class of techniques for modeling relations between blocks of observed variables by means of latent variables, kernelized PLS methods have proved to be efficient in treating nonlinear data. By establishing the relations between sets of observed variables, KPLS can provide a general purpose HAM model, which construct nonlinear regression models in possibly high-dimensional feature spaces and uses a few latent factors to account for most of the variations in the response. For face recognition problem, we apply the HAM model to efficiently characterize each subject by relating some possible variations of a face image to a given face, using a modular strudcture by assigning an independent model to each subject. Several benchmark face databases have been used to test the performance.