Author: Harriat Christa, G.; Jesica, J.; Anisha, K.; Sagayam, K. M.
Title: CNN-based mask detection system using OpenCV and MobileNetV2 Cord-id: cvr4j81x Document date: 2021_1_1
ID: cvr4j81x
Snippet: this paper establishes a 'Safety system for mask detection during this COVID-19 pandemic'. Face mask detection has seen an overwhelming growth in the realm of Computer vision and deep learning, since the unprecedented COVID-19 global pandemic that has mandated wearing masks in public places. To tackle the situation, machine learning engineers have come up with several algorithms and techniques to identify unmasked individuals using various mask detection models. The proposed approach in this pap
Document: this paper establishes a 'Safety system for mask detection during this COVID-19 pandemic'. Face mask detection has seen an overwhelming growth in the realm of Computer vision and deep learning, since the unprecedented COVID-19 global pandemic that has mandated wearing masks in public places. To tackle the situation, machine learning engineers have come up with several algorithms and techniques to identify unmasked individuals using various mask detection models. The proposed approach in this paper adopts frameworks of deep learning, TensorFlow, Keras, and OpenCV libraries to detect face masks in real time. The trained MobileNet model, presented in this paper, yielded an accuracy score of 0.99 and an F1 score of 0.99 in the training data. This user-friendly model can be incorporated with several existing technologies such as face detection, biometric authentication and facial expression detection for further advancements in the future. © 2021 IEEE.
Search related documents:
Co phrase search for related documents, hyperlinks ordered by date