Mask-invariant Face Recognition through Template-level Knowledge Distillation

TL;DR

This paper introduces MaskInv, a face recognition method that maintains high accuracy despite masks by using template-level knowledge distillation and margin-based loss, outperforming state-of-the-art solutions.

Contribution

The paper proposes a novel mask-invariant face recognition approach utilizing template-level knowledge distillation and margin-based loss, improving robustness against masked faces.

Findings

Outperforms previous SOTA in masked face recognition challenges.

Effective on both real and synthetic masked face datasets.

Maintains good performance on unmasked faces with minimal loss.

Abstract

The emergence of the global COVID-19 pandemic poses new challenges for biometrics. Not only are contactless biometric identification options becoming more important, but face recognition has also recently been confronted with the frequent wearing of masks. These masks affect the performance of previous face recognition systems, as they hide important identity information. In this paper, we propose a mask-invariant face recognition solution (MaskInv) that utilizes template-level knowledge distillation within a training paradigm that aims at producing embeddings of masked faces that are similar to those of non-masked faces of the same identities. In addition to the distilled knowledge, the student network benefits from additional guidance by margin-based identity classification loss, ElasticFace, using masked and non-masked faces. In a step-wise ablation study on two real masked face databases and five mainstream databases with synthetic masks, we prove the rationalization of our MaskInv approach. Our proposed solution outperforms previous state-of-the-art (SOTA) academic solutions in the recent MFRC-21 challenge in both scenarios, masked vs masked and masked vs non-masked, and also outperforms the previous solution on the MFR2 dataset. Furthermore, we demonstrate that the proposed model can still perform well on unmasked faces with only a minor loss in verification performance. The code, the trained models, as well as the evaluation protocol on the synthetically masked data are publicly available: https://github.com/fdbtrs/Masked-Face-Recognition-KD.