  In this paper we present a simple novel approach to tackle the challenges of
scaling and rotation of face images in face recognition. The proposed approach
registers the training and testing visual face images by log-polar
transformation, which is capable to handle complicacies introduced by scaling
and rotation. Log-polar images are projected into eigenspace and finally
classified using an improved multi-layer perceptron. In the experiments we have
used ORL face database and Object Tracking and Classification Beyond Visible
Spectrum (OTCBVS) database for visual face images. Experimental results show
that the proposed approach significantly improves the recognition performances
from visual to log-polar-visual face images. In case of ORL face database,
recognition rate for visual face images is 89.5% and that is increased to 97.5%
for log-polar-visual face images whereas for OTCBVS face database recognition
rate for visual images is 87.84% and 96.36% for log-polar-visual face images.
