From Data to Insights: A Covariate Analysis of the IARPA BRIAR Dataset for Multimodal Biometric Recognition Algorithms at Altitude and Range
<|reference_start|>From Data to Insights: A Covariate Analysis of the IARPA BRIAR Dataset for Multimodal Biometric Recognition Algorithms at Altitude and Range: 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.<|reference_end|>
@article{bolme2024from,
    title={From Data to Insights: A Covariate Analysis of the IARPA BRIAR Dataset
  for Multimodal Biometric Recognition Algorithms at Altitude and Range},
    author={David S. Bolme, Deniz Aykac, Ryan Shivers, Joel Brogan, Nell Barber,
  Bob Zhang, Laura Davies, and David Cornett III},
    journal={arXiv preprint arXiv:2409.01514},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.01514},
    primaryClass={cs.CV cs.AI cs.LG}
}