Comparison between Thermal and Visible Facial Features on a Verification Approach

A comprehensible performance analysis of a thermal and visible face verification system based on the Scale-Invariant Feature Transform algorithm (SIFT) with a vocabulary tree is presented in this work, 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 and the visible image per subject, containing 1,476 thermal images and 1,476 visible images equally split into two sets of modalities: face and head, respectively. 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 for both ranges. This approach has reached accuracy rates of 97.60% in thermal head images in relation to 88.20% in thermal face verification. For visible range, 99.05% with visible head images in relation to 97.65% in visible face verification. In this proposal and after experiments, visible range gives better accuracy than thermal range, and with independency of range, head images give the most discriminate information.