IROLS Based Radial Basis Function Neural Network for Face Recognition

Abstract

Face representation (FR) plays a typically important role in face recognition and methods such as principal component analysis (PCA) and linear discriminant analysis (LDA) have been received wide attention recently.These FR methods will inevitably lead to poor classification performance in case of great facial variations such as expression, lighting, occlusion and so on, due to the fact that the image gray value matrices on which they manipulate are very sensitive to these facial variations .The recognition of faces is very important because of its potential commercial applications, such as in the area of video surveillance, access control systems, retrieval of an identity from a data base for criminal investigations and user authentication.The recognition performance of the face recognition system deteriorates when the system is exposed to the real world scenario.This problem happens because we do not have a complete set of training samples that consists of all types of visual variations.Furthermore, the extendibility of the system to recognize more new people who join the existing groups in the future may cause a problem to the system.In this work, a radial basis function (RBF) neural network with a new incremental learning method based on the regularized orthogonal least square (ROLS) algorithm is proposed for face recognition.It is designed to accommodate new information without retraining the initial network.In addition, it accumulates previous experience and learns updated new knowledge of the existing groups to increase the robustness of the system.The proposed work is to be developed on Matlab platform for its realization.