# Expanding on the BRIAR Dataset: A Comprehensive Whole Body Biometric Recognition Resource at Extreme Distances and Real-World Scenarios (Collections 1-4)

arXiv: https://arxiv.org/abs/2501.14070 · HTML: https://mlsanity.com/paper/2501.14070/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## Builds on (most cited first, 9 of 9)

- [End-to-End Human Pose and Mesh Reconstruction with Transformers](https://mlsanity.com/paper/2012.09760/) (2020)
- [Distribution-Aware Coordinate Representation for Human Pose Estimation](https://mlsanity.com/paper/1910.06278/) (2019)
- [Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification](https://mlsanity.com/paper/2007.10315/) (2020)
- [Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Dataset](https://mlsanity.com/paper/2211.01917/) (2022)
- [Single Frame Atmospheric Turbulence Mitigation: A Benchmark Study and A New Physics-Inspired Transformer Model](https://mlsanity.com/paper/2207.10040/) (2022)
- [MEVID: Multi-view Extended Videos with Identities for Video Person Re-Identification](https://mlsanity.com/paper/2211.04656/) (2022)
- [Gait Recognition Using 3-D Human Body Shape Inference](https://mlsanity.com/paper/2212.09042/) (2022)
- [From Data to Insights: A Covariate Analysis of the IARPA BRIAR Dataset for Multimodal Biometric Recognition Algorithms at Altitude and Range](https://mlsanity.com/paper/2409.01514/) (2024)
- [Long-Range Biometric Identification in Real World Scenarios: A Comprehensive Evaluation Framework Based on Missions](https://mlsanity.com/paper/2409.01540/) (2024)

## Built on by (most cited first, 3 of 3)

- [50 Years of Automated Face Recognition](https://mlsanity.com/paper/2505.24247/) (2025)
- [Person Recognition at Altitude and Range: Fusion of Face, Body Shape and Gait](https://mlsanity.com/paper/2505.04616/) (2025)
- [TransFIRA: Transfer Learning for Face Image Recognizability Assessment](https://mlsanity.com/paper/2510.06353/) (2025)

## Abstract (arXiv)

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.