Person Recognition at Altitude and Range: Fusion of Face, Body Shape and Gait
Journal:
arXiv
Published Date:
May 7, 2025
Abstract
We address the problem of whole-body person recognition in unconstrained
environments. This problem arises in surveillance scenarios such as those in
the IARPA Biometric Recognition and Identification at Altitude and Range
(BRIAR) program, where biometric data is captured at long standoff distances,
elevated viewing angles, and under adverse atmospheric conditions (e.g.,
turbulence and high wind velocity). To this end, we propose FarSight, a unified
end-to-end system for person recognition that integrates complementary
biometric cues across face, gait, and body shape modalities. FarSight
incorporates novel algorithms across four core modules: multi-subject detection
and tracking, recognition-aware video restoration, modality-specific biometric
feature encoding, and quality-guided multi-modal fusion. These components are
designed to work cohesively under degraded image conditions, large pose and
scale variations, and cross-domain gaps. Extensive experiments on the BRIAR
dataset, one of the most comprehensive benchmarks for long-range, multi-modal
biometric recognition, demonstrate the effectiveness of FarSight. Compared to
our preliminary system, this system achieves a 34.1% absolute gain in 1:1
verification accuracy ([email protected]% FAR), a 17.8% increase in closed-set
identification (Rank-20), and a 34.3% reduction in open-set identification
errors (FNIR@1% FPIR). Furthermore, FarSight was evaluated in the 2025 NIST RTE
Face in Video Evaluation (FIVE), which conducts standardized face recognition
testing on the BRIAR dataset. These results establish FarSight as a
state-of-the-art solution for operational biometric recognition in challenging
real-world conditions.
