  This paper presents a novel approach to handle the challenges of face
recognition. In this work thermal face images are considered, which minimizes
the affect of illumination changes and occlusion due to moustache, beards,
adornments etc. The proposed approach registers the training and testing
thermal face images in polar coordinate, which is capable to handle
complicacies introduced by scaling and rotation. Polar 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 face images. Experimental results
show that the proposed approach significantly improves the verification and
identification performance and the success rate is 97.05%.
