Face mask recognition with convolution neural network methods

Name: Chetsada Chongthanachote

LCSH: Authentication

LCSH: Neural networks (Computer science)

Abstract: Nowadays, the practice of verifying the identity of a person or knowing your customer is widespread and legally applicable. This makes entrepreneurs have to deal with the application that supports their customers. Using Al technology to check a customer’s face is one of the most common ways to identify them. As a result, this will reduce the work time of employees. By 2021, facial recognition will be in use at the top 20 U.S. airports for 100% of international passengers, including American citizens. This is according to an executive order from Runaway Suitcase. เท this research, we put emphasis on procedure for distinguishing between people who wore masks did not wear masks. There is preparing process for overlaying mask to face image before bringing to the classification mode. We discovered that self-prepared datasets for images of persons wearing masks can be utilized as an input to the classification model and achieve the desired result, Flowever the VGG-16 model can identify people both wearing masks and not wearing masks effective than ResNet50 model

King Mongkut's Institute of Technology Ladkrabang. Central Library

Address: BANGKOK

Email: Lifelong@kmitl.ac.th

Name: Yuwadee Klomwises

Role: Thesis Advisor

Created: 2022

Modified: 2023-05-22

Issued: 2023-05-19

วิทยานิพนธ์/Thesis

application/pdf

eng

DegreeName: Master of Science

Level: Master's Degree

Descipline: Data Science and Analytics

Grantor: King Mongkut's Institute of Technology Ladkrabang

©copyrights King Mongkut's Institute of Technology Ladkrabang

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