Face Recognition Beyond the Visible Spectrum

The facial vascular network is highly characteristic to the individual, much like the way his fingerprint is. A non-obtrusive way to capture this information is through thermal imaging. The convective heat transfer effect from the flow of “hot” arterial blood in superficial vessels creates characteristic thermal imprints, which are at a gradient with the surrounding tissue. This casts sigmoid edges on the human tissue where major blood vessels are present. We present an algorithmic methodology to extract and represent the facial vasculature. The methodology combines image morphology and probabilistic inference. The morphology captures the overall structure of the vascular network while the probabilistic part reflects the positional uncertainty for the vessel walls, due to the phenomenon of thermal diffusion. The accuracy of the methodology is tested through extensive experimentation and meticulous ground-truthing. Furthermore, the efficacy of this information for identity recognition is tested on substantial databases.