Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Dataset

David Cornett, Joel Brogan, Nell Barber, Deniz Aykac, Seth Baird, Nicholas Burchfield, Carl Dukes, Andrew Duncan, Regina Ferrell, Jim Goddard, Gavin Jager, Matthew Larson, Bart Murphy, Christi Johnson, Ian Shelley, Nisha Srinivas, Brandon Stockwell, Leanne Thompson, Matthew Yohe, Robert Zhang, Scott Dolvin, Hector J. Santos-Villalobos, David S. Bolme; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2023, pp. 593-602

Face recognition technology has advanced significantlyin recent years due largely to the availability of largeand 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, specializedlong-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.