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Face masks have long been used in many areas of everyday life to protectagainst the inhalation of hazardous fumes and particles. They also offer aneffective solution in healthcare for bi-directional protection againstair-borne diseases. Wearing and positioning the mask correctly is essential forits function. Convolutional neural networks (CNNs) offer an excellent solutionfor face recognition and classification of correct mask wearing andpositioning. In the context of the ongoing COVID-19 pandemic, such algorithmscan be used at entrances to corporate buildings, airports, shopping areas, andother indoor locations, to mitigate the spread of the virus. These applicationscenarios impose major challenges to the underlying compute platform. Theinference hardware must be cheap, small and energy efficient, while providingsufficient memory and compute power to execute accurate CNNs at a reasonablylow latency. To maintain data privacy of the public, all processing must remainon the edge-device, without any communication with cloud servers. To addressthese challenges, we present a low-power binary neural network classifier forcorrect facial-mask wear and positioning. The classification task isimplemented on an embedded FPGA, performing high-throughput binary operations.Classification can take place at up to ~6400 frames-per-second, easily enablingmulti-camera, speed-gate settings or statistics collection in crowd settings.When deployed on a single entrance or gate, the idle power consumption isreduced to 1.6W, improving the battery-life of the device. We achieve anaccuracy 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 theirability to generalize the relevant features over all subjects using theGrad-CAM approach.

BinaryCoP: Binary Neural Network-based COVID-19 Face-Mask Wear and Positioning Predictor on Edge Devices