Face Recognition Using Kernel PCA and Hierarchical RBF Network

Abstract

This paper proposes a new face recognition approach by using kernel principal component analysis (KPCA) and hierarchical radial basis function (HRBF) network classification model. To improve the quality of the face images, a series of image pre-processing techniques, which include histogram equalization, edge detection and geometrical transformation are used. The KPCA is employed to extract features for reducing the dimension of the face pattern, and the HRBF network is used to identify the faces. To accelerate the convergence of the HRBF network and improve the quality of the solutions, the extended compact genetic programming (ECGP) and particle swarm optimization (PSO) is applied to optimize the HRBF network structure and parameters. The experimental results show that the proposed framework is efficient for face recognition.