Weakly Supervised Face and Whole Body Recognition in Turbulent Environments

• URL: http://arxiv.org/abs/2308.11757v1
• Date: Tue, 22 Aug 2023 19:58:02 GMT
• Title: Weakly Supervised Face and Whole Body Recognition in Turbulent Environments
• Authors: Kshitij Nikhal, Benjamin S. Riggan
• Abstract summary: We propose a new weakly supervised framework that generates domain representations, aligning turbulent and pristine images into a common subspace. We also introduce a new tilt map estimator that predicts geometric distortions observed in turbulent images. Our method does not require synthesizing turbulent-free images or ground-truth paired images, and requires significantly fewer annotated samples.
• Score: 2.2263723609685773
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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