Classification of Polar-Thermal Eigenfaces using Multilayer Perceptron for Human Face Recognition

TL;DR

This paper introduces a novel thermal face recognition method using polar coordinate registration, eigenspace projection, and multilayer perceptron classification, achieving a 97.05% success rate on OTCBVS database.

Contribution

It proposes a new thermal face recognition approach that handles illumination, occlusion, scaling, and rotation challenges with polar coordinate registration and neural network classification.

Findings

Achieved 97.05% success rate on OTCBVS database.

Significantly improved face verification and identification performance.

Effective handling of illumination, occlusion, scaling, and rotation issues.

Abstract

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%.