Indian Masked Faces in the Wild Dataset

@article{Mishra2021IndianMF,
  title={Indian Masked Faces in the Wild Dataset},
  author={Shiksha Mishra and Puspita Majumdar and Richa Singh and Mayank Vatsa},
  journal={2021 IEEE International Conference on Image Processing (ICIP)},
  year={2021},
  pages={884-888}
}
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… 

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References

SHOWING 1-10 OF 24 REFERENCES

Masked Face Recognition Dataset and Application

A multi-granularity masked face recognition model is developed that achieves 95% accuracy, exceeding the results reported by the industry and is currently the world's largest real-world masked face dataset.

Detecting Masked Faces in the Wild with LLE-CNNs

This paper introduces a dataset, denoted as MAFA, with 30, 811 Internet images and 35, 806 masked faces, and proposes LLE-CNNs for masked face detection, which consist of three major modules.

VGGFace2: A Dataset for Recognising Faces across Pose and Age

A new large-scale face dataset named VGGFace2 is introduced, which contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each subject, and the automated and manual filtering stages to ensure a high accuracy for the images of each identity are described.

Occluded Face Recognition in the Wild by Identity-Diversity Inpainting

This paper proposes identity-diversity inpainting to facilitate occluded face recognition by integrating GAN with an optimized pre-trained CNN recognizer which serves as the third player to compete with the generator by distinguishing diversity within the same identity class.

Generate to Adapt: Resolution Adaption Network for Surveillance Face Recognition

RAN which contains Multi-Resolution Generative Adversarial Networks (MR-GAN) followed by a feature adaption network with translation gate is developed to fuse the discriminative information of LR faces into backbone network, while preserving the discrimination ability of original face representations.

A survey of face recognition techniques under occlusion

This paper introduces face detection under occlusions, a preliminary step in face recognition, and presents how existing face recognition methods cope with the occlusion problem and classify them into three categories.

Learning Face Representation from Scratch

A semi-automatical way to collect face images from Internet is proposed and a large scale dataset containing about 10,000 subjects and 500,000 images, called CASIAWebFace is built, based on which a 11-layer CNN is used to learn discriminative representation and obtain state-of-theart accuracy on LFW and YTF.

Face De-Occlusion Using 3D Morphable Model and Generative Adversarial Network

  • Xiaowei YuanI. Park
  • Computer Science
    2019 IEEE/CVF International Conference on Computer Vision (ICCV)
  • 2019
A novel method is proposed to restore de-occluded face images based on inverse use of 3DMM and generative adversarial network and combine a global and local adversarial convolutional neural network to learn face de-OCclusion model.

FaceNet: A unified embedding for face recognition and clustering

A system that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure offace similarity, and achieves state-of-the-art face recognition performance using only 128-bytes perface.