Weakly Supervised Face and Whole Body Recognition in Turbulent
  Environments
Kshitij Nikhal Benjamin S. Riggan
Face and person recognition have recently achieved remarkable success under
challenging scenarios, such as off-pose and cross-spectrum matching. However,
long-range recognition systems are often hindered by atmospheric turbulence,
leading to spatially and temporally varying distortions in the image. Current
solutions rely on generative models to reconstruct a turbulent-free image, but
often preserve photo-realism instead of discriminative features that are
essential for recognition. This can be attributed to the lack of large-scale
datasets of turbulent and pristine paired images, necessary for optimal
reconstruction. To address this issue, we propose a new weakly supervised
framework that employs a parameter-efficient self-attention module to generate
domain agnostic representations, aligning turbulent and pristine images into a
common subspace. Additionally, we introduce a new tilt map estimator that
predicts geometric distortions observed in turbulent images. This estimate is
used to re-rank gallery matches, resulting in up to 13.86\% improvement in
rank-1 accuracy. Our method does not require synthesizing turbulent-free images
or ground-truth paired images, and requires significantly fewer annotated
samples, enabling more practical and rapid utility of increasingly large
datasets. We analyze our framework using two datasets -- Long-Range Face
Identification Dataset (LRFID) and BRIAR Government Collection 1 (BGC1) --
achieving enhanced discriminability under varying turbulence and standoff
distance.