Person recognition at a distance entails recognizing the identity of an individual appearing in images or videos collected by long-range imaging systems such as drones or surveillance cameras. Despite recent advances in deep convolutional neural networks (DCNNs), this remains challenging. Images or videos collected by long-range cameras often suffer from atmospheric turbulence, blur, low-resolution, unconstrained poses, and poor illumination. In this paper, we provide a brief survey of recent advances in person recognition at a distance. In particular, we review recent work in multi-spectral face verification, person re-identification, and gait-based analysis techniques. Furthermore, we discuss the merits and drawbacks of existing approaches and identify important, yet under explored challenges for deploying remote person recognition systems in-the-wild.
@article{arxiv.2212.08969,
title = {A Brief Survey on Person Recognition at a Distance},
author = {Chrisopher B. Nalty and Neehar Peri and Joshua Gleason and Carlos D. Castillo and Shuowen Hu and Thirimachos Bourlai and Rama Chellappa},
journal= {arXiv preprint arXiv:2212.08969},
year = {2022}
}
Comments
This work has been accepted to the IEEE Asilomar Conference on Signals, Systems, and Computers (ACSSC) 2022