Muhammad Ezzuddeen, Suratman (2022) Investigate And Analysis Of Deep Learning And Machine Learning Algorithm For Face Mask Detection System. College of Engineering, Universiti Malaysia Pahang Al-Sultan Abdullah.
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EA18017_MUHAMMAD EZZUDDEEN BIN SURATMAN_Thesis - Ezzuddeen Dean.pdf - Accepted Version Download (3MB) | Preview |
Abstract
People nowadays tend to wear a protective facemask because of the pandemic COVID-19 that strike our world few years ago and wear protective facemask has become a new normal. Many public place that provides a certain service want people to wear mask correctly before entering the place. Therefore, by developing the facemask detection system, it tends to help a global society to aware the environment that surround by the virus and to prevent the infections. Although vaccines have been developed, people still need to be aware because of some society that stick not to wanting a vaccine. For develop this system, machine learning and deep learning is the best method to use by using some basic machine learning package such as Tensorflow, Keras and OpenCV. This method detects the image of someone face from the image, video and real time monitoring correctly and then identifies it has a facemask on it or not and will alert the authority if not wearing a facemask. This system can be use at the premise before people entering the place and would eliminate the need to place a worker to monitor the people coming in at the entrance and minimize the infections.
| Item Type: | Undergraduates Project Papers |
|---|---|
| Additional Information: | SV: Prof. Madya Ir. Ts. Dr. Fahmi Bin Samsuri |
| Uncontrolled Keywords: | pandemic, face mask detection |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | College of Engineering |
| Depositing User: | Mr. Nik Ahmad Nasyrun Nik Abd Malik |
| Date Deposited: | 05 Jan 2024 04:00 |
| Last Modified: | 05 Jan 2024 04:00 |
| URI: | http://umpir.ump.edu.my/id/eprint/39869 |
| Download Statistic: | View Download Statistics |
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