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The objective of this work is to recognize faces using sets of images in visual and thermal spectra. This is challenging because the former is greatly affected by illumination changes, while the latter frequently contains occlusions due to eye-wear and is inherently less discriminative. Our method is based on a fusion of the two modalities. 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 test set of 29 individuals and extreme illumination changes.