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.

提出了一个新的弱监督框架，采用参数高效的自注意力模块来生成领域不可知的表示，将湍流和清晰图像对齐到一个共同的子空间中，引入了一个新的倾斜映射估计器，用于预测湍流图像中观察到的几何失真，在画廊匹配中重新排序，排名1准确度提高了13.86%，无需生成无湍流的图像或真实配对的图像，仅需要少量的注释样本，并使得越来越大的数据集更具实用性和迅速性。

在动荡环境中的弱监督人脸和全身识别