  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.
