Classification of Log-Polar-Visual Eigenfaces using Multilayer Perceptron

TL;DR

This paper introduces a novel face recognition method that uses log-polar transformation and eigenspace projection combined with a multilayer perceptron to improve recognition accuracy under scaling and rotation variations.

Contribution

The paper presents a new approach integrating log-polar transformation with eigenspace projection and an improved multilayer perceptron for robust face recognition.

Findings

Recognition rate improved to 97.5% on ORL database

Recognition rate increased to 96.36% on OTCBVS database

Significant performance enhancement over traditional methods

Abstract

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.