The objective of this work is to authenticate individuals based on the appearance of their faces. This is a difficult pattern recognition problem because facial appearance is generally greatly affected by the changes in the way a face is illuminated, by the camera viewpoint and partial occlusions, for example due to eye-wear. We describe a fully automatic algorithm that systematically addresses each of these challenges. The main novelty is an algorithm for decision-level fusion of two types of imagery: one acquired in the visual and one acquired in infrared electromagnetic spectrum. Specifically: we examine: (i) the effects of preprocessing of data in each domain, (ii) the fusion of holistic and local facial appearance, and (iii) propose an algorithm for combining the similarity scores in visual and thermal spectra in the presence of prescription glasses and significant pose variations, using a small number of training images (5-7). Our system achieved a high correct identification rate of 97% on a freely available data set containing extreme illumination changes.