Hyperspectral imaging provides extensive spectral reflectance information useful for material classification and discrimination not available with conventional broadband imaging. In this work, we first seek to characterize the hyperspectral signature of human faces in the shortwave infrared (SWIR) band. A hyperspectral SWIR face dataset of 100 subjects was collected as part of this study. Regions of interest (ROI) were defined for each subject and the mean and variance of each ROI were computed. The results show that hyperspectral signatures are similar between male and female subjects for the cheek, forehead, and hair ROIs. Furthermore, this study investigated whether the hyperspectral face signatures from the ROIs contained discriminative information for gender classification. We implemented and trained five different classifiers for gender classification. Results from the machine learning experiments indicate that hyperspectral facial signatures in the SWIR band is only weakly discriminative with respect to gender.