# BinaryCoP: Binary Neural Network-based COVID-19 Face-Mask Wear and Positioning Predictor on Edge Devices arXiv: https://arxiv.org/abs/2102.03456 ยท HTML: https://mlsanity.com/paper/2102.03456/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 17 of its 33 references that are in the map. - First posted on arXiv 6 Feb 2021. ## Builds on (most cited first, 17 of 17) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016) - [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015) - [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013) - [XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks](https://mlsanity.com/paper/1603.05279/) (2016) - [BinaryConnect: Training Deep Neural Networks with binary weights during propagations](https://mlsanity.com/paper/1511.00363/) (2015) - [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020) - [FINN: A Framework for Fast, Scalable Binarized Neural Network Inference](https://mlsanity.com/paper/1612.07119/) (2016) - [Towards Accurate Binary Convolutional Neural Network](https://mlsanity.com/paper/1711.11294/) (2017) - [Deep Learning vs. Traditional Computer Vision](https://mlsanity.com/paper/1910.13796/) (2019) - [CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images](https://mlsanity.com/paper/2004.04931/) (2020) - [Masked Face Recognition Dataset and Application](https://mlsanity.com/paper/2003.09093/) (2020) - [Masked Face Recognition for Secure Authentication](https://mlsanity.com/paper/2008.11104/) (2020) - [MaskedFace-Net -- A Dataset of Correctly/Incorrectly Masked Face Images in the Context of COVID-19](https://mlsanity.com/paper/2008.08016/) (2020) - [Binary DAD-Net: Binarized Driveable Area Detection Network for Autonomous Driving](https://mlsanity.com/paper/2006.08178/) (2020) - [WearMask: Fast In-browser Face Mask Detection with Serverless Edge Computing for COVID-19](https://mlsanity.com/paper/2101.00784/) (2021) - [Towards Fast and Energy-Efficient Binarized Neural Network Inference on FPGA](https://mlsanity.com/paper/1810.02068/) (2018) ## Built on by (most cited first, 7 of 7) - [A comprehensive review of Binary Neural Network](https://mlsanity.com/paper/2110.06804/) (2021) - [APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores](https://mlsanity.com/paper/2106.12169/) (2021) - [BitGNN: Unleashing the Performance Potential of Binary Graph Neural Networks on GPUs](https://mlsanity.com/paper/2305.02522/) (2023) - [BCNN: Binary Complex Neural Network](https://mlsanity.com/paper/2104.10044/) (2021) - [GAAF: Searching Activation Functions for Binary Neural Networks through Genetic Algorithm](https://mlsanity.com/paper/2206.03291/) (2022) - [Searching Similarity Measure for Binarized Neural Networks](https://mlsanity.com/paper/2206.03325/) (2022) - [An experimental comparative study of backpropagation and alternatives for training binary neural networks for image classification](https://mlsanity.com/paper/2408.04460/) (2024) ## Abstract (arXiv) Face masks have long been used in many areas of everyday life to protect against the inhalation of hazardous fumes and particles. They also offer an effective solution in healthcare for bi-directional protection against air-borne diseases. Wearing and positioning the mask correctly is essential for its function. Convolutional neural networks (CNNs) offer an excellent solution for face recognition and classification of correct mask wearing and positioning. In the context of the ongoing COVID-19 pandemic, such algorithms can be used at entrances to corporate buildings, airports, shopping areas, and other indoor locations, to mitigate the spread of the virus. These application scenarios impose major challenges to the underlying compute platform. The inference hardware must be cheap, small and energy efficient, while providing sufficient memory and compute power to execute accurate CNNs at a reasonably low latency. To maintain data privacy of the public, all processing must remain on the edge-device, without any communication with cloud servers. To address these challenges, we present a low-power binary neural network classifier for correct facial-mask wear and positioning. The classification task is implemented on an embedded FPGA, performing high-throughput binary operations. Classification can take place at up to ~6400 frames-per-second, easily enabling multi-camera, speed-gate settings or statistics collection in crowd settings. When deployed on a single entrance or gate, the idle power consumption is reduced to 1.6W, improving the battery-life of the device. We achieve an accuracy of up to 98% for four wearing positions of the MaskedFace-Net dataset. To maintain equivalent classification accuracy for all face structures, skin-tones, hair types, and mask types, the algorithms are tested for their ability to generalize the relevant features over all subjects using the Grad-CAM approach.