Classification of thermal face images using radial basis function neural network

Abstract

In this paper we have investigated an approach to recognize thermal face images for face recognition using line features and Radial Basis Function (RBF) neural network as classifier for them. The proposed method comprises of three steps. In the first step, line features are extracted from thermal polar images and feature vectors are constructed using these line. In the second step feature vectors thus obtained are passed through eigenspace projection for the dimensionality reduction of feature vectors. Finally, the images projected into eigenspace are classified using a Radial Basis Function (RBF) neural network. In the experiments we have used Object Tracking and Classification beyond Visible Spectrum (OTCBVS) database. Experimental results show that the proposed approach significantly improves the verification and identification performance and the maximum success rate is 100% whereas on an average it is 94.44%.