  Face recognition technology has advanced significantly in recent years due
largely to the availability of large and increasingly complex training datasets
for use in deep learning models. These datasets, however, typically comprise
images scraped from news sites or social media platforms and, therefore, have
limited utility in more advanced security, forensics, and military
applications. These applications require lower resolution, longer ranges, and
elevated viewpoints. To meet these critical needs, we collected and curated the
first and second subsets of a large multi-modal biometric dataset designed for
use in the research and development (R&D) of biometric recognition technologies
under extremely challenging conditions. Thus far, the dataset includes more
than 350,000 still images and over 1,300 hours of video footage of
approximately 1,000 subjects. To collect this data, we used Nikon DSLR cameras,
a variety of commercial surveillance cameras, specialized long-rage R&D
cameras, and Group 1 and Group 2 UAV platforms. The goal is to support the
development of algorithms capable of accurately recognizing people at ranges up
to 1,000 m and from high angles of elevation. These advances will include
improvements to the state of the art in face recognition and will support new
research in the area of whole-body recognition using methods based on gait and
anthropometry. This paper describes methods used to collect and curate the
dataset, and the dataset's characteristics at the current stage.
