  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%.
