This paper describes a system by which Unmanned Aerial Vehicles (UAVs) can gather high-quality face images that can be used in biometric identification tasks. Success in face-based identification depends in large part on the image quality, and a major factor is how frontal the view is. Face recognition software pipelines can improve identification rates by synthesizing frontal views from non-frontal views by a process call {\em frontalization}. Here we exploit the high mobility of UAVs to actively gather frontal images using components of a synthetic frontalization pipeline. We define a frontalization error and show that it can be used to guide an UAVs to capture frontal views. Further, we show that the resulting image stream improves matching quality of a typical face recognition similarity metric. The system is implemented using an off-the-shelf hardware and software components and can be easily transfered to any ROS enabled UAVs.
@article{arxiv.2102.08542,
title = {Active Face Frontalization using Commodity Unmanned Aerial Vehicles},
author = {Nagashri Lakshminarayana and Yifang Liu and Karthik Dantu and Venu Govindaraju and Nils Napp},
journal= {arXiv preprint arXiv:2102.08542},
year = {2021}
}