  This paper presents a concept of image pixel fusion of visual and thermal
faces, which can significantly improve the overall performance of a face
recognition system. Several factors affect face recognition performance
including pose variations, facial expression changes, occlusions, and most
importantly illumination changes. So, image pixel fusion of thermal and visual
images is a solution to overcome the drawbacks present in the individual
thermal and visual face images. Fused images are projected into eigenspace and
finally classified using a multi-layer perceptron. In the experiments we have
used Object Tracking and Classification Beyond Visible Spectrum (OTCBVS)
database benchmark thermal and visual face images. Experimental results show
that the proposed approach significantly improves the verification and
identification performance and the success rate is 95.07%. The main objective
of employing fusion is to produce a fused image that provides the most detailed
and reliable information. Fusion of multiple images together produces a more
efficient representation of the image.
