- By:
- Cornett, David C; Glenn, Gavin A; Aykac, Deniz ; Johnson, Christi R; Zhang, Robert Z; Shivers, Ryan M; Bolme, David S; Davies, Laura M; Dolvin, Scott S; Barber, Cornelia L; Brogan, Joel R; Burchfield, Nicholas R; Dukes, Carl L; Duncan, Andrew M; Ferrell, Regina K; Garrett, Austin C; Goddard Jr, James S; Hines, Jairus B; Murphy, Bart L; Pharris, Sean D; Stockwell, Brandon M; Thompson, Leanne ; Yohe, Matthew A
- Page Number:
- 1-9
- Book Title:
- 2025 IEEE 19th International Conference on Automatic Face and Gesture Recognition
- Publication Date:
- March 12, 2026
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- IEEE International Conference on Automatic Face and Gesture Recognition (FG)
- Conference Location:
- Clearwater, Florida, United States of America
- Conference Sponsor:
- IEEE Biometrics Council
- View DOI Listing:
- https://doi.org/10.1109/FG61629.2025.11099141
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.
