Abstract
The state-of-the-art in biometric recognition algorithms and operational systems has advanced quickly in recent years providing high accuracy and robustness in more challenging collection environments and consumer applications. However, the technology still suffers greatly when applied to non-conventional settings such as those seen when performing identification at extreme distances or from elevated cameras on buildings or mounted to UAVs. This paper summarizes an extension to the largest dataset currently focused on addressing these operational challenges, and describes its composition as well as methodologies of collection, curation, and annotation.
| Original language | English |
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| Title of host publication | 2025 IEEE 19th International Conference on Automatic Face and Gesture Recognition, FG 2025 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9798331553418 |
| DOIs | |
| State | Published - 2025 |
| Event | 19th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2025 - Tampa, United States Duration: May 26 2025 → May 30 2025 |
Publication series
| Name | 2025 IEEE 19th International Conference on Automatic Face and Gesture Recognition, FG 2025 |
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Conference
| Conference | 19th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2025 |
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| Country/Territory | United States |
| City | Tampa |
| Period | 05/26/25 → 05/30/25 |
Funding
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. This research used resources from the Knowledge Discovery Infrastructure housed at Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The U.S. government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. 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 U.S. 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). The authors of this paper also wish to acknowledge the contributions of: Knowledge Discovery Infrastructure (KDI) Staff: Dallas Sacca and Ryan Tipton. Proctors and drivers: Raymond Borges-Hink, Nancy Engle, Dale Hensley, Nikki Jones, Michael Jones, Marylin Langston, Matt Love, Amanda Mottern, Gio Pascascio, Linda Paschal, Christina Peshoff, Donna Pierce, Ryan Styles, and Lauren Torkelson. UAS Pilots: Joe Baldwin, Kase Clapp, Dakota Haldeman, Andrew Harter, Amanda Killingsworth, Matt Larson, Genevieve Martin, Aaron O Toole, Jason Richards, and Brad Stinson. Health and Safety Support Staff: Margaret Smith and Miranda Liner.