Multi-Dataset Benchmarks for Masked Identification using Contrastive Representation Learning

• URL: http://arxiv.org/abs/2106.05596v1
• Date: Thu, 10 Jun 2021 08:58:10 GMT
• Title: Multi-Dataset Benchmarks for Masked Identification using Contrastive Representation Learning
• Authors: Sachith Seneviratne, Nuran Kasthuriaarachchi, Sanka Rasnayaka
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The COVID-19 pandemic has drastically changed accepted norms globally. Within the past year, masks have been used as a public health response to limit the spread of the virus. This sudden change has rendered many face recognition based access control, authentication and surveillance systems ineffective. Official documents such as passports, driving license and national identity cards are enrolled with fully uncovered face images. However, in the current global situation, face matching systems should be able to match these reference images with masked face images. As an example, 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 find that current facial recognition techniques are not robust to this form of occlusion. To address this unique requirement presented due to the current circumstance, we propose a set of re-purposed datasets and a benchmark for researchers to use. We also propose a contrastive visual representation learning based pre-training workflow which is specialized to masked vs unmasked face matching. We ensure that our method learns robust features to differentiate people across varying data collection scenarios. We achieve this by training over many different datasets and validating our result by testing on various holdout datasets. The specialized weights trained by our method outperform standard face recognition features for masked to unmasked face matching. We believe the provided synthetic mask generating code, our novel training approach and the trained weights from the masked face models will help in adopting existing face recognition systems to operate in the current global environment. We open-source all contributions for broader use by the research community.

Related papers

• An Exploratory Study of Masked Face Recognition with Machine Learning Algorithms [0.0]
  Using face masks have become crucial in our daily life with the recent world-wide COVID-19 pandemic. The effect of mask-wearing in face recognition is yet an understudied issue. We use six conventional machine learning algorithms, which are SVC, KNN, LDA, DT, LR and NB, to find out the ones which perform best.
  arXiv  Detail & Related papers  (2023-06-14T14:50:23Z)
• OPOM: Customized Invisible Cloak towards Face Privacy Protection [58.07786010689529]
  We investigate the face privacy protection from a technology standpoint based on a new type of customized cloak. We propose a new method, named one person one mask (OPOM), to generate person-specific (class-wise) universal masks. The effectiveness of the proposed method is evaluated on both common and celebrity datasets.
  arXiv  Detail & Related papers  (2022-05-24T11:29:37Z)
• 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)
• MaskMTL: Attribute prediction in masked facial images with deep multitask learning [9.91045425400833]
  This paper presents a deep Multi-Task Learning (MTL) approach to jointly estimate various heterogeneous attributes from a single masked facial image. The proposed approach supersedes in performance to other competing techniques.
  arXiv  Detail & Related papers  (2022-01-09T13:03:29Z)
• 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)
• 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)
• 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)
• Masked Face Recognition for Secure Authentication [2.429066522170765]
  Masked faces make it difficult to be detected and recognized, thereby threatening to make the in-house datasets invalid. We present an open-source tool, MaskTheFace to mask faces effectively creating a large dataset of masked faces. We report an increase of 38% in the true positive rate for the Facenet system.
  arXiv  Detail & Related papers  (2020-08-25T15:33:59Z)
• 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.