Face Recognition Under Drone Surveillance Concerning Turbulence (FaceDrone 2022 Workshop)
(The topics covered by the workshop, with a description which specify the goals and the technical issues that the workshop aims to address.
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Face recognition has received a lot of attention due to surveillance needed across a variety of security platforms ranging from border control to e-payments to secure office access. However, in the large crowd gathering in the festivals or the gaming events, identification of any possible suspects involving any avoidable mishappenings highly depends on the facial information. The information might not be effectively captured using the traditional surveillance cameras due to their significant distance from the gathering event locations. For that, the use of drone sensors is an ideal solution. However, the acquired images generally suffer from poor quality. The one probable reason for that is the effect of environmental factors such as turbulence. In this workshop coupled with the challenge session (accepted with the same title), we want to make the first step towards unconstrained surveillance using face recognition. The challenge and session will not only help the development of novel algorithms needed to improve the face recognition performance but also disseminate the knowledge to the audience towards the possible future directions for real-world face recognition systems.
Face recognition in drone-shot videos has applicability in scenarios such as identifying individuals stuck at remote locations or in crowded places monitored via a drone. Recently, IARPA’s Biometric Recognition and Identification at Altitude and Range (BRIAR) program [1] also emphasizes the challenging problem of identifying individuals from long-range at elevated platforms. The DroneSURF dataset contains over 200 videos of 58 subjects captured across 411K frames. A pre-defined protocol is provided with the dataset, where 34 subjects form the training partition, and the remaining are used for testing. Along with the drone-shot videos, the dataset also contains four HR face images of each subject as the gallery images. Two protocols have been provided: (i) Active surveillance: where the drone actively follows the subjects, and (ii) Passive surveillance: where the drone monitors a particular area.
In brief, the goal of the workshop is to advance face recognition in challenging drone surveillance settings. The workshop will consist of the papers from the best performing algorithms in the challenge and the new papers with novel techniques to improve drone face recognition.
Submission details will be released soon. Each paper needs to follow the official guidelines including the submission template (https://www.icpr2022.com/submission-guidelines/)