Paper Title
REAL TIME FACE MASK DETECTION AND ACCURACY PREDICTION
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Registration ID: IJNRD_189083
Published ID: IJNRD2303279
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Keywords
Open cv, mask Detection,OpenCV Library,Image Recognition,Tensor Flow, Convolutional Neural Network,python,machine learning
Abstract
Wearing a face mask appears to be important to both the person and other people to restrict the spread of a virulent disease that can be communicated via sputtering in the context of a virulent sickness which can be transmitted via sputtering. During the COVID-19 pandemic, people all around the world were required to wear masks to limit the danger of virus transmission. According to research, properly wearing a face mask can effectively reduce transmission and save lives. The identification of face masks has made significant progress in the domains of image processing and computer vision since the start of the Covid-19 pandemic. Face recognition models have been built using a number of algorithms and methodologies. This is a deep learning model design using Python, TensorFlow, Keras, MobileNet, and openCV in this project. The main goal is to utilise a trained AI model and a collection of masked and unmasked photos to determine whether or not someone is wearing a mask during a webcam Livestream. Because it is relatively resource efficient to deploy, this model can be employed for safety considerations
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How To Cite (APA)
Sanapala Anusha, Reddy Harithasri, Samardi Preethi, Sappa Prameela, & Dr.P.Himakeerthi (March-2023). REAL TIME FACE MASK DETECTION AND ACCURACY PREDICTION. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(3), c706-c711. https://ijnrd.org/papers/IJNRD2303279.pdf
Issue
Volume 8 Issue 3, March-2023
Pages : c706-c711
Other Publication Details
Paper Reg. ID: IJNRD_189083
Published Paper Id: IJNRD2303279
Research Area: Computer Science & TechnologyÂ
Author Type: Indian Author
Country: Srikakulam, Andhra Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2303279.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2303279
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