Publication detail

Detecting Faces With Face Masks

PŘINOSIL, J. MALÝ, O.

Original Title

Detecting Faces With Face Masks

Type

conference paper

Language

English

Original Abstract

This paper deals with the evaluation of several methods for face detection when the face is covered by a mask. The methods evaluated are Haar cascade and Histogram of Oriented Gradients as feature-based approaches, Multitask Cascade Convolutional Neural Network, Max Margin Object Detection and TinyFace as convolutional neural network based approaches. Various types of face masks are considered: disposal face mask, burka, balaclava, ski helmet with ski goggles, hockey helmet with protective grill, costumes, and others. The TinyFace method achieves the best accuracy result, but also requires much more computational power than other approaches. Therefore, this paper describes an experiment to see if the accuracy of some of the remaining methods can be improved by retraining their models with new image data containing faces with various face masks.

Keywords

face detection, facial mask, convolutional networks, mask categories

Authors

PŘINOSIL, J.; MALÝ, O.

Released

28. 7. 2021

ISBN

978-1-6654-2933-7

Book

2021 44th International Conference on Telecommunications and Signal Processing (TSP)

Pages from

259

Pages to

262

Pages count

4

URL

https://ieeexplore.ieee.org/document/9522677

BibTex

@inproceedings{BUT175496,
  author="Jiří {Přinosil} and Ondřej {Malý}",
  title="Detecting Faces With Face Masks",
  booktitle="2021 44th International Conference on Telecommunications and Signal Processing (TSP)",
  year="2021",
  pages="259--262",
  doi="10.1109/TSP52935.2021.9522677",
  isbn="978-1-6654-2933-7",
  url="https://ieeexplore.ieee.org/document/9522677"
}