Thermal Face Recognition under Spatial Variation Conditions

Abstract

This paper proposes a novel method for degraded thermal face recognition using Hu Li moments. The method deals with spatial variations of thermal images resulting from low resolution and changes in head pose. We describe thermal images with a set of scalar quantities that can capture its significant features at component level. Each thermal image is divided into components where the statistical features of these components are combined using a fusion method. The method finds a combination of multiple statistical patterns to produce an integrated result that is enhanced in terms of information content for correct classification. We show that local representations using Hu Li moments provide higher discriminability and offer robustness against variability due to spatial changes. To evaluate the performance of the proposed method, we conduct thorough experiments and detailed analysis on a database consisting of 7500 images of different poses and variable spatial resolutions. This database is generated from an initial one that originally consists of 1500 images, by down-sampling the original images to four different low resolution levels beside the original level. The results show that the proposed method achieves significantly high recognition rates over various conditions resembling practical scenarios.