# From Data to Insights: A Covariate Analysis of the IARPA BRIAR Dataset for Multimodal Biometric Recognition Algorithms at Altitude and Range

arXiv: https://arxiv.org/abs/2409.01514 · HTML: https://mlsanity.com/paper/2409.01514/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 3 Sep 2024.

## Builds on (most cited first, 8 of 8)

- [Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Dataset](https://mlsanity.com/paper/2211.01917/) (2022)
- [FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude](https://mlsanity.com/paper/2306.17206/) (2023)
- [ShARc: Shape and Appearance Recognition for Person Identification In-the-wild](https://mlsanity.com/paper/2310.15946/) (2023)
- [Recognizing People by Body Shape Using Deep Networks of Images and Words](https://mlsanity.com/paper/2305.19160/) (2023)
- [Whole-body Detection, Recognition and Identification at Altitude and Range](https://mlsanity.com/paper/2311.05725/) (2023)
- [Activity-Biometrics: Person Identification from Daily Activities](https://mlsanity.com/paper/2403.17360/) (2024)
- [A Brief Survey on Person Recognition at a Distance](https://mlsanity.com/paper/2212.08969/) (2022)
- [Weakly Supervised Face and Whole Body Recognition in Turbulent Environments](https://mlsanity.com/paper/2308.11757/) (2023)

## Built on by (most cited first, 6 of 6)

- [Person Recognition at Altitude and Range: Fusion of Face, Body Shape and Gait](https://mlsanity.com/paper/2505.04616/) (2025)
- [Expanding on the BRIAR Dataset: A Comprehensive Whole Body Biometric Recognition Resource at Extreme Distances and Real-World Scenarios (Collections 1-4)](https://mlsanity.com/paper/2501.14070/) (2025)
- [Person Recognition in Aerial Surveillance: A Decade Survey](https://mlsanity.com/paper/2511.17674/) (2025)
- [Combo-Gait: Unified Transformer Framework for Multi-Modal Gait Recognition and Attribute Analysis](https://mlsanity.com/paper/2510.10417/) (2025)
- [On Pairwise Quantile Regression - Statistical Guarantees and Applications](https://mlsanity.com/paper/2607.04431/) (2026)
- [Restore What Matters: Lessons from Joint Restoration and Recognition](https://mlsanity.com/paper/2609.13791/) (2026)

## Abstract (arXiv)

This paper examines covariate effects on fused whole body biometrics performance in the IARPA BRIAR dataset, specifically focusing on UAV platforms, elevated positions, and distances up to 1000 meters. The dataset includes outdoor videos compared with indoor images and controlled gait recordings. Normalized raw fusion scores relate directly to predicted false accept rates (FAR), offering an intuitive means for interpreting model results. A linear model is developed to predict biometric algorithm scores, analyzing their performance to identify the most influential covariates on accuracy at altitude and range. Weather factors like temperature, wind speed, solar loading, and turbulence are also investigated in this analysis. The study found that resolution and camera distance best predicted accuracy and findings can guide future research and development efforts in long-range/elevated/UAV biometrics and support the creation of more reliable and robust systems for national security and other critical domains.