Mask-invariant Face Recognition through Template-level Knowledge Distillation

• URL: http://arxiv.org/abs/2112.05646v1
• Date: Fri, 10 Dec 2021 16:19:28 GMT
• Title: Mask-invariant Face Recognition through Template-level Knowledge Distillation
• Authors: Marco Huber, Fadi Boutros, Florian Kirchbuchner, Naser Damer
• Abstract summary: Masks affect the performance of previous face recognition systems. We propose a mask-invariant face recognition solution (MaskInv) In addition to the distilled knowledge, the student network benefits from additional guidance by margin-based identity classification loss.
• Score: 3.727773051465455
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Seeing through the Mask: Multi-task Generative Mask Decoupling Face Recognition [47.248075664420874]
  Current general face recognition system suffers from serious performance degradation when encountering occluded scenes. This paper proposes a Multi-task gEnerative mask dEcoupling face Recognition (MEER) network to jointly handle these two tasks. We first present a novel mask decoupling module to disentangle mask and identity information, which makes the network obtain purer identity features from visible facial components.
  arXiv  Detail & Related papers  (2023-11-20T03:23:03Z)
• Learning Representations for Masked Facial Recovery [8.124282476398843]
  pandemic of these recent years has led to a dramatic increase in people wearing protective masks in public venues. One way to address the problem is to revert to face recovery methods as a preprocessing step. We introduce a method that is specific for the recovery of the face image from an image of the same individual wearing a mask.
  arXiv  Detail & Related papers  (2022-12-28T22:22:15Z)
• FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders [81.21440457805932]
  We propose a novel framework FaceMAE, where the face privacy and recognition performance are considered simultaneously. randomly masked face images are used to train the reconstruction module in FaceMAE. We also perform sufficient privacy-preserving face recognition on several public face datasets.
  arXiv  Detail & Related papers  (2022-05-23T07:19:42Z)
• Adversarial Mask: Real-World Adversarial Attack Against Face Recognition Models [66.07662074148142]
  We propose a physical adversarial universal perturbation (UAP) against state-of-the-art deep learning-based facial recognition models. In our experiments, we examined the transferability of our adversarial mask to a wide range of deep learning models and datasets. We validated our adversarial mask effectiveness in real-world experiments by printing the adversarial pattern on a fabric medical face mask.
  arXiv  Detail & Related papers  (2021-11-21T08:13:21Z)
• FocusFace: Multi-task Contrastive Learning for Masked Face Recognition [4.420321822469077]
  SARS-CoV-2 has presented direct and indirect challenges to the scientific community. Face recognition methods struggle to perform identity verification with similar accuracy on masked and unmasked individuals. We propose FocusFace, a multi-task architecture that uses contrastive learning to be able to accurately perform masked face recognition.
  arXiv  Detail & Related papers  (2021-10-28T08:17:12Z)
• MLFW: A Database for Face Recognition on Masked Faces [56.441078419992046]
  Masked LFW (MLFW) is a tool to generate masked faces from unmasked faces automatically. The recognition accuracy of SOTA models declines 5%-16% on MLFW database compared with the accuracy on the original images.
  arXiv  Detail & Related papers  (2021-09-13T09:30:10Z)
• Multi-Dataset Benchmarks for Masked Identification using Contrastive Representation Learning [0.0]
  COVID-19 pandemic has drastically changed accepted norms globally. Official documents such as passports, driving license and national identity cards are enrolled with fully uncovered face images. In an airport or security checkpoint it is safer to match the unmasked image of the identifying document to the masked person rather than asking them to remove the mask. We propose a contrastive visual representation learning based pre-training workflow which is specialized to masked vs unmasked face matching.
  arXiv  Detail & Related papers  (2021-06-10T08:58:10Z)
• Contrastive Context-Aware Learning for 3D High-Fidelity Mask Face Presentation Attack Detection [103.7264459186552]
  Face presentation attack detection (PAD) is essential to secure face recognition systems. Most existing 3D mask PAD benchmarks suffer from several drawbacks. We introduce a largescale High-Fidelity Mask dataset to bridge the gap to real-world applications.
  arXiv  Detail & Related papers  (2021-04-13T12:48:38Z)
• Unmasking Face Embeddings by Self-restrained Triplet Loss for Accurate Masked Face Recognition [6.865656740940772]
  We present a solution to improve the masked face recognition performance. Specifically, we propose the Embedding Unmasking Model (EUM) operated on top of existing face recognition models. We also propose a novel loss function, the Self-restrained Triplet (SRT), which enabled the EUM to produce embeddings similar to these of unmasked faces of the same identities.
  arXiv  Detail & Related papers  (2021-03-02T13:43:11Z)
• Towards Face Encryption by Generating Adversarial Identity Masks [53.82211571716117]
  We propose a targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks. TIP-IM provides 95%+ protection success rate against various state-of-the-art face recognition models.
  arXiv  Detail & Related papers  (2020-03-15T12:45:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.