Classification of Fused Images using Radial Basis Function Neural Network for Human Face Recognition

TL;DR

This paper presents a face recognition method that fuses thermal and visual images, then classifies them using a radial basis function neural network, achieving high accuracy on benchmark datasets.

Contribution

It introduces a novel fusion technique combined with RBF neural network classification for improved face recognition accuracy.

Findings

Achieved up to 96% recognition success rate.

Effective fusion of thermal and visual images enhances recognition.

Method outperforms some existing approaches on OTCBVS dataset.

Abstract

Here an efficient fusion technique for automatic face recognition has been presented. Fusion of visual and thermal images has been done to take the advantages of thermal images as well as visual images. By employing fusion a new image can be obtained, which provides the most detailed, reliable, and discriminating information. In this method fused images are generated using visual and thermal face images in the first step. In the second step, fused images are projected into eigenspace and finally classified using a radial basis function neural network. In the experiments Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark for thermal and visual face images have been used. Experimental results show that the proposed approach performs well in recognizing unknown individuals with a maximum success rate of 96%.