Automatic Face Mask Detection Using Deep Learning-Based Mobile-Net Architecture
Sandeep Kumar, Shilpa Rani, Arpit Jain, Munish Kumar, Poonam Jaglan
Abstract
COVID-19 affects public health and economies around the world. Proper wearing of masks plays a vital role in daily life to overcome this epidemic situation. To reduce human contamination, adequate people monitoring is required in public places, i.e., malls, railway stations, bus stands, markets, airports, universities, etc. In highly populated areas, manual inspection of the face mask is not easy, so an automatic face mask detection model helps detect the face mask. This paper proposed mobile Net architecture for face mask detection. This advanced architecture considers the colour image from the standard and its own database for extracting the face feature in-depth to identify the face mask. To validate our work, we used five-fold cross-validation techniques. Finally, the accuracy achieved by the proposed work on two standard databases is 96.4% and 95.7%, showing the outperforming face mask detection results.