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In this paper we present a simple novel approach to tackle the challenges ofscaling and rotation of face images in face recognition. The proposed approachregisters the training and testing visual face images by log-polartransformation, which is capable to handle complicacies introduced by scalingand rotation. Log-polar images are projected into eigenspace and finallyclassified using an improved multi-layer perceptron. In the experiments we haveused ORL face database and Object Tracking and Classification Beyond VisibleSpectrum (OTCBVS) database for visual face images. Experimental results showthat the proposed approach significantly improves the recognition performancesfrom 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 recognitionrate for visual images is 87.84% and 96.36% for log-polar-visual face images.

Classification of Log-Polar-Visual Eigenfaces using Multilayer Perceptron