We examine the performance of illumination-invariant face recognition in outdoor hyperspectral images using a database of 200 subjects. The hyperspectral camera acquires 31 bands over the 700- to 1000-nm spectral range. Faces are represented by local spectral information for several tissue types. Illumination variation is modeled by low-dimensional spectral radiance subspaces. Weighted invariant subspace projection over multiple tissue types is used for recognition. The experiments consider various face orientations and expressions. The analysis includes experiments for images synthesized from indoor face reflectance images of 200 subjects, using a database of more than 7,000 outdoor illumination spectra. We also examine a set of images of 10 subjects of the 200 that were acquired under outdoor conditions using a calibrated hyperspectral camera.