MFR 2021: Masked Face Recognition Competition

TL;DR

The MFR 2021 competition evaluated diverse solutions for improving masked face recognition accuracy, highlighting advancements in model performance and deployability in real-world scenarios.

Contribution

This paper summarizes the first masked face recognition competition, showcasing diverse approaches and their effectiveness on a real masked face dataset.

Findings

10 out of 18 solutions outperformed existing academic methods in accuracy

Solutions varied in model size and complexity, emphasizing deployability

The competition provided a benchmark for masked face recognition performance

Abstract

This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attracted a total of 10 participating teams with valid submissions. The affiliations of these teams are diverse and associated with academia and industry in nine different countries. These teams successfully submitted 18 valid solutions. The competition is designed to motivate solutions aiming at enhancing the face recognition accuracy of masked faces. Moreover, the competition considered the deployability of the proposed solutions by taking the compactness of the face recognition models into account. A private dataset representing a collaborative, multi-session, real masked, capture scenario is used to evaluate the submitted solutions. In comparison to one of the top-performing academic face recognition solutions, 10 out of the 18 submitted solutions did score higher masked face verification accuracy.