{'fraction_non_alphanumeric': 0.021739130434782608, 'fraction_numerical': 0.011956521739130435, 'mean_word_length': 4.6503067484662575, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Face-Mask Detection to Control the COVID-19 Spread Employing Deep Learning Approach

The world is in distress because of the COVID-19 pandemic; the best remedy is isolation from this infection. Wearing a face mask is vital to protect ourselves from the virus. A person wearing a face mask can be detected by Computer Vision and notify the authorities if the protocol is not followed. Since it is very labor-intensive to check if a person is wearing a face mask in public places, Computer Vision makes it easy for real-time monitoring. This method uses MobileNet and OpenCV to classify people into two categories, wearing the face mask or not, with 99.41% and 100% testing accuracy for face mask detection and without face mask detection, respectively. It requires low computing power and is compatible with mobile devices, which can help the concerned authorities enforce the rule of wearing face masks in public places.