Active Face Frontalization using Commodity Unmanned Aerial Vehicles
This work addresses the challenge of obtaining high-quality frontal face images for biometric identification, particularly in scenarios where UAVs are used, but it is incremental as it builds on existing frontalization methods.
The paper tackles the problem of improving face recognition by using UAVs to actively capture frontal face images, showing that this approach enhances matching quality in biometric identification tasks.
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.