id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-158415-qwxyuuz7	Chavda, Amit	Multi-Stage CNN Architecture for Face Mask Detection	2020-09-16		.txt	text/plain	3182	205	63	Our system consists of a dual-stage Convolutional Neural Network (CNN) architecture capable of detecting masked and unmasked faces and can be integrated with pre-installed CCTV cameras. Convolutional Neural Networks (CNNs) (LeCun et al., 1998 ) is a key aspect in modern Computer Vision tasks like pattern object detection, image classification, pattern recognition tasks, etc. Multiple algorithms based on Regional Proposal Network like Fast RCNN (Girshick, 2015) and Faster RCNN (Ren et al., 2015) have achieved higher accuracy and better results than most single stage detectors. The detected faces (regions of interest) extracted from this stage are then batched together and passed to the second stage of our architecture, which is a CNN based Face Mask Classifier. The output of this stage is an image (or video frame) with localized faces, classified as masked or unmasked.	./cache/cord-158415-qwxyuuz7.txt	./txt/cord-158415-qwxyuuz7.txt