Thermal face verification based on scale-invariant feature transform and vocabulary tree: Application to Biometric Verification Systems

Abstract

This paper presents a comprehensible performance analysis of a thermal face verification system based on the Scale-Invariant Feature Transform algorithm (SIFT) with a vocabulary tree, providing a verification scheme that scales efficiently to a large number of features. The image database is formed from front-view thermal images, which contain facial temperature distributions of different individuals in 2-dimensional format, containing 1,476 thermal images equally split into two sets of modalities: face and head. The SIFT features are not only invariant to image scale and rotation but also essential for providing a robust matching across changes in illumination or addition of noise. Descriptors extracted from local regions are hierarchically set in a vocabulary tree using the k-means algorithm as clustering method. That provides a larger and more discriminatory vocabulary, which leads to a performance improvement. The verification quality is evaluated through a series of independent experiments with various results, showing the power of the system, which satisfactorily verifies the identity of the database subjects and overcoming limitations such as dependency on illumination conditions and facial expressions. A comparison between head and face verification is made, obtaining success rates of 97.60% with thermal head images in relation to 88.20% in thermal face verification.