Indian Masked Faces in the Wild Dataset

• URL: http://arxiv.org/abs/2106.09670v1
• Date: Thu, 17 Jun 2021 17:23:54 GMT
• Title: Indian Masked Faces in the Wild Dataset
• Authors: Shiksha Mishra, Puspita Majumdar, Richa Singh, Mayank Vatsa
• Abstract summary: 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.
• Score: 86.79402670904338
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the COVID-19 pandemic, wearing face masks has become a mandate in public places worldwide. Face masks occlude a significant portion of the facial region. Additionally, people wear different types of masks, from simple ones to ones with graphics and prints. These pose new challenges to face recognition algorithms. Researchers have recently proposed a few masked face datasets for designing algorithms to overcome the challenges of masked face recognition. However, existing datasets lack the cultural diversity and collection in the unrestricted settings. Country like India with attire diversity, people are not limited to wearing traditional masks but also clothing like a thin cotton printed towel (locally called as ``gamcha''), ``stoles'', and ``handkerchiefs'' to cover their faces. In this paper, we present a novel \textbf{Indian 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. Experimental results demonstrate the limitations of existing algorithms in presence of diverse conditions.

Related papers

• Face Mask Removal with Region-attentive Face Inpainting [0.7433327915285965]
  We propose a generative face inpainting method to recover/reconstruct the masked part of a face. Our proposed method includes a Multi-scale Channel-Spatial Attention Module (M-CSAM) to mitigate the spatial information loss. We synthesize our own Masked-Faces dataset from the CelebA dataset by incorporating five different types of face masks.
  arXiv  Detail & Related papers  (2024-09-10T20:10:11Z)
• 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)
• 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)
• 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)
• 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)
• 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 InsightFace Track Report [79.77020394722788]
  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.
  arXiv  Detail & Related papers  (2021-08-18T15:14:44Z)
• 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)
• MaskedFace-Net -- A Dataset of Correctly/Incorrectly Masked Face Images in the Context of COVID-19 [2.7528170226206443]
  The wearing of the face masks appears as a solution for limiting the spread of COVID-19. To perform this task, a large dataset of masked faces is necessary for training deep learning models towards detecting people wearing masks and those not wearing masks. Some large datasets of masked faces are available in the literature. However, at the moment, there are no available large dataset of masked face images that permits to check if detected masked faces are correctly worn or not.
  arXiv  Detail & Related papers  (2020-08-18T16:38:11Z)

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.