Open AccessPosted Content
The Effect of Wearing a Face Mask on Face Image Quality
TLDR
In this article, the effect of wearing a face mask on the performance of face recognition has been investigated in a collaborative environment, where state-of-the-art face image quality assessment methods of different natures were used.Abstract:
Due to the COVID-19 situation, face masks have become a main part of our
daily life. Wearing mouth-and-nose protection has been made a mandate in many
public places, to prevent the spread of the COVID-19 virus. However, face masks
affect the performance of face recognition, since a large area of the face is
covered. The effect of wearing a face mask on the different components of the
face recognition system in a collaborative environment is a problem that is
still to be fully studied. This work studies, for the first time, the effect of
wearing a face mask on face image quality by utilising state-of-the-art face
image quality assessment methods of different natures. This aims at providing
better understanding on the effect of face masks on the operation of face
recognition as a whole system. In addition, we further studied the effect of
simulated masks on face image utility in comparison to real face masks. We
discuss the correlation between the mask effect on face image quality and that
on the face verification performance by automatic systems and human experts,
indicating a consistent trend between both factors. The evaluation is conducted
on the database containing (1) no-masked faces, (2) real face masks, and (3)
simulated face masks, by synthetically generating digital facial masks on
no-masked faces according to the NIST protocols [1, 23]. Finally, a visual
interpretation of the face areas contributing to the quality score of a
selected set of quality assessment methods is provided to give a deeper insight
into the difference of network decisions in masked and non-masked faces, among
other variations.read more
Citations
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Posted Content
Face Image Quality Assessment: A Literature Survey
Torsten Schlett,Christian Rathgeb,Olaf Henniger,Javier Galbally,Julian Fierrez,Christoph Busch +5 more
TL;DR: This survey provides an overview of the face image quality assessment literature, which predominantly focuses on visible wavelength face image input and a trend towards deep learning based methods is observed, including notable conceptual differences among the recent approaches.
Posted Content
FocusFace: Multi-task Contrastive Learning for Masked Face Recognition
TL;DR: FocusFace as discussed by the authors is a multi-task architecture that uses contrastive learning to accurately perform masked face recognition, which is designed to be trained from scratch or to work on top of state-of-the-art face recognition methods without sacrificing the capabilities of existing models in conventional face recognition tasks.
Posted Content
Partial Attack Supervision and Regional Weighted Inference for Masked Face Presentation Attack Detection.
TL;DR: Wang et al. as mentioned in this paper proposed a method that considers partial attack labels to supervise the PAD model training, as well as regional weighted inference to further improve the mask face presentation detection performance by varying the focus on different facial areas.
References
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