Active Face Frontalization using Commodity Unmanned Aerial Vehicles

TL;DR

This paper presents a UAV-based system that actively captures high-quality, frontal face images to enhance biometric identification, utilizing a synthetic frontalization pipeline guided by a defined error metric.

Contribution

It introduces a novel UAV-guided frontalization approach that improves face recognition by actively capturing frontal images using a synthetic pipeline.

Findings

UAVs can effectively gather frontal face images for biometric tasks.

The frontalization error guides UAVs to optimize image capture.

Captured images improve face recognition matching accuracy.

Abstract

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.