Design of face recognition algorithm realized with feature extraction from 2D-LDA and optimized polynomial-based RBF NNs

Abstract

This study elaborates on a design of a face recognition algorithm realized with feature extraction from 2D-LDA and the use of polynomial-based radial basis function neural networks (P-RBF NNS). The overall face recognition system consists of two modules such as the preprocessing part and recognition part. The proposed polynomial-based radial basis function neural networks is used as an the recognition part of the overall face recognition system, while a data preprocessing algorithm presented of 2 dimensional linear discriminant analysis (2D-LDA) is exploited to data preprocessing. The essential design parameters are optimized by means of differential evolution (DE). The experimental results for benchmark face datasets - the Yale and ORL database - demonstrate the effectiveness and efficiency of 2D-LDA algorithm compared with other approaches such as principal component analysis (PCA), and fusion of PCA-LDA.