A comparative study of feature extraction methods and their application to P-RBF NNs in face recognition problem

Abstract

This study is concerned with a design of a face recognition algorithm realized based on feature extraction with the aid of the 2-directional 2-dimensional linear discriminant analysis referred to as (2D)2LDA. The 2DLDA algorithm yields high accuracy, but comes with an unresolved problem of handling large feature matrices present in face recognition problems. The proposed (2D)2LDA algorithm is computationally more efficient and produces more powerful discrimination decision. The proposed P-RBF NNs is used as a recognition module. The architecture of this module consists of three functional components. The coefficients of the P-RBF NNs model are obtained by fuzzy inference method forming the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum, fuzzification coefficient, and the feature selection mechanism) of the networks are optimized by means of differential evolution (DE). The experimental results reported for benchmark face datasets – the Yale, ORL database, and IC&CI dataset demonstrate the effectiveness and efficiency of (2D)2LDA algorithm compared with other pre-processing approaches such as LPP, 2D-PCA, 2D-LPP, SR, PCA, (2D)2PCA and fusion of PCA-LDA. The experimental results show that (2D)2LDA based P-RBF NNs achieves higher performance than being reported by other methods.