Paper on FPGA-based COVID-19 Face-Mask Detection accepted at RAW 2021


In the context of the ongoing COVID-19 pandemic, face masks offer an effective contribution to healthcare. Wearing and positioning the mask correctly is essential for its function. Convolutional neural networks (CNNs) offer an excellent solution for visual face recognition and classification of correct mask wearing and positioning. In their paper entitled "BinaryCoP: Binary Neural Network-based COVID-19 Face-Mask Wear and Positioning Predictor on Edge Devices", the authors have investigated an embedded FPGA accelerator for such task, optimizing throughput and power consumption.

The authors are Nael Fasfous, Manoj-Rohit Vemparala, Alexander Frickenstein, Lukas Frickenstein, Mohamed Badawy, and Walter Stechele. The paper has been accepted for publication at the Reconfigurable Architectures Workshop, a well recognized forum for leading research in reconfigurable computing.

Reconfigurable Architectures Workshop, May 17, 2021 (https://raw.necst.it/)