Abstract
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 plat-forms and, therefore, have limited utility in more advanced security, forensics, and military applications. These applications require lower resolution, longer ranges, and ele-vated 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 technolo-gies 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 sub-jects. 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 ad-vances 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 col-lect and curate the dataset, and the dataset's characteristics at the current stage.
| Original language | English |
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| Title of host publication | Proceedings - 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2023 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 593-602 |
| Number of pages | 10 |
| ISBN (Electronic) | 9798350320565 |
| DOIs | |
| State | Published - 2023 |
| Event | 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2023 - Waikoloa, United States Duration: Jan 3 2023 → Jan 7 2023 |
Publication series
| Name | Proceedings - 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2023 |
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Conference
| Conference | 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2023 |
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| Country/Territory | United States |
| City | Waikoloa |
| Period | 01/3/23 → 01/7/23 |
Funding
Acknowledgements and Copyright. The authors would like to acknowledge the contributions and assistance of the following organizations and individuals in their support of the creation of the BRIAR dataset: US Government Agencies: NIST: Mei Ngan, Patrick Grother — Army C2ISR: Kevin Miller, Tim Williams Additional Support: ORNL: Margaret Smith, Amanda Mottern, Christy Gambrell, Michaela Martin, Chris Gibbs, Willy Besancenez — UCOR: Steve Nolan — Guardian Centers of Georgia This research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via D20202007300010. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/ doe-public-access-plan).