Open AccessPosted Content
Multi-Dataset Benchmarks for Masked Identification using Contrastive Representation Learning
TLDR
In this article, the authors proposed a contrastive visual representation learning based pre-training workflow which is specialized to masked vs unmasked face matching and ensure that their method learns robust features to differentiate people across varying data collection scenarios.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.read more
Citations
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Book ChapterDOI
Does a Face Mask Protect My Privacy?: Deep Learning to Predict Protected Attributes from Masked Face Images
TL;DR: In this paper , a CNN based on the ResNet-50 architecture with 20,003 synthetic masked images was used to predict sex (94.7%), race (83.1%), and age (MAE 6.21 and RMSE 8.33) from masked face images.
Proceedings ArticleDOI
Age Estimation with Synthetic Mask Generation Based on MobileNet and Facial Keypoint Detection
TL;DR: In this article , an improved convolutional neural network architecture based on MobileNet is proposed to perform age estimation, which achieves MAE of 3.79 and 6.54 on unmasked and masked faces, respectively, which demonstrates the effectiveness of the proposed model.
Proceedings ArticleDOI
An Exploratory Study of Masked Face Recognition with Machine Learning Algorithms
TL;DR: In this paper , the effect of mask-wearing in face recognition is addressed by evaluating the performance of a number of face recognition models which are tested by identifying masked and unmasked face images.
References
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Posted Content
A Simple Framework for Contrastive Learning of Visual Representations
TL;DR: It is shown that composition of data augmentations plays a critical role in defining effective predictive tasks, and introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning.
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Deep Learning Face Attributes in the Wild
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