Masked Face Recognition Challenge: The InsightFace Track Report

• URL: http://arxiv.org/abs/2108.08191v1
• Date: Wed, 18 Aug 2021 15:14:44 GMT
• Title: Masked Face Recognition Challenge: The InsightFace Track Report
• Authors: Jiankang Deng and Jia Guo and Xiang An and Zheng Zhu and Stefanos Zafeiriou
• Abstract summary: During the COVID-19 coronavirus epidemic, almost everyone wears a facial mask, which poses a huge challenge to deep face recognition. In this workshop, we focus on bench-marking deep face recognition methods under the existence of facial masks.
• Score: 79.77020394722788
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: During the COVID-19 coronavirus epidemic, almost everyone wears a facial mask, which poses a huge challenge to deep face recognition. In this workshop, we organize Masked Face Recognition (MFR) challenge and focus on bench-marking deep face recognition methods under the existence of facial masks. In the MFR challenge, there are two main tracks: the InsightFace track and the WebFace260M track. For the InsightFace track, we manually collect a large-scale masked face test set with 7K identities. In addition, we also collect a children test set including 14K identities and a multi-racial test set containing 242K identities. By using these three test sets, we build up an online model testing system, which can give a comprehensive evaluation of face recognition models. To avoid data privacy problems, no test image is released to the public. As the challenge is still under-going, we will keep on updating the top-ranked solutions as well as this report on the arxiv.

Related papers

• Towards Large-scale Masked Face Recognition [44.380235958577785]
  During the COVID-19 coronavirus epidemic, almost everyone is wearing masks, which poses a huge challenge for deep learning-based face recognition algorithms. In this paper, we will present our textbfchampionship solutions in ICCV MFR WebFace260M and InsightFace unconstrained tracks.
  arXiv  Detail & Related papers  (2023-10-25T05:04:47Z)
• Surveillance Face Presentation Attack Detection Challenge [68.06719263243806]
  Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. We collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) SuHiFiMask contains $10,195$ videos from $101$ subjects of different age groups, which are collected by $7$ mainstream surveillance cameras. We organize a face presentation attack detection challenge in surveillance scenarios.
  arXiv  Detail & Related papers  (2023-04-15T15:23:19Z)
• WebFace260M: A Benchmark for Million-Scale Deep Face Recognition [89.39080252029386]
  We contribute a new million-scale recognition benchmark, containing uncurated 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) A distributed framework is developed to train face recognition models efficiently without tampering with the performance. The proposed benchmark shows enormous potential on standard, masked and unbiased face recognition scenarios.
  arXiv  Detail & Related papers  (2022-04-21T14:56:53Z)
• Mask-invariant Face Recognition through Template-level Knowledge Distillation [3.727773051465455]
  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.
  arXiv  Detail & Related papers  (2021-12-10T16:19:28Z)
• A realistic approach to generate masked faces applied on two novel masked face recognition data sets [14.130698536174767]
  We propose a method for enhancing data sets containing faces without masks by creating synthetic masks and overlaying them on faces in the original images. We employ our method to generate a number of 445,446 (90%) samples of masks for the CASIA-WebFace data set and 196,254 (96.8%) masks for the CelebA data set. We show that our method produces significantly more realistic training examples of masks overlaid on faces by asking volunteers to qualitatively compare it to other methods or data sets.
  arXiv  Detail & Related papers  (2021-09-03T22:33:55Z)
• Masked Face Recognition Challenge: The WebFace260M Track Report [81.57455766506197]
  Face Bio-metrics under COVID Workshop and Masked Face Recognition Challenge in ICCV 2021. WebFace260M Track aims to push the frontiers of practical MFR. In the first phase of WebFace260M Track, 69 teams (total 833 solutions) participate in the challenge. There are second phase of the challenge till October 1, 2021 and on-going leaderboard.
  arXiv  Detail & Related papers  (2021-08-16T15:51:51Z)
• Indian Masked Faces in the Wild Dataset [86.79402670904338]
  We present a novel textbfIndian Masked Faces in the Wild (IMFW) dataset which contains images with variations in pose, illumination, resolution, and the variety of masks worn by the subjects. We have also benchmarked the performance of existing face recognition models on the proposed IMFW dataset.
  arXiv  Detail & Related papers  (2021-06-17T17:23:54Z)
• 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)
• RetinaFaceMask: A Single Stage Face Mask Detector for Assisting Control of the COVID-19 Pandemic [1.2691047660244335]
  One effective strategy to prevent infection for people is to wear masks in public places. Certain public service providers require clients to use their services only if they properly wear masks. There are, however, only a few research studies on automatic face mask detection. We proposed RetinaFaceMask, the first high-performance single stage face mask detector.
  arXiv  Detail & Related papers  (2020-05-08T10:45:16Z)

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.