Face recognition employees attendance system
Face recognition uses a variety of technologies and locations to carry out the attendance system. In order to recognise a face in real-time settings utilising a specific purpose device, attendance systems require accurate results. Video architecture is also achieved in our design by piercing the cam...
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2022
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| Online Access: | http://eprints.uthm.edu.my/6983/ http://eprints.uthm.edu.my/6983/1/24p%20MUNEF%20HASAN%20ABDULLAH%20AL%20NASSER.pdf http://eprints.uthm.edu.my/6983/2/MUNEF%20HASAN%20ABDULLAH%20AL%20NASSER%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/6983/3/MUNEF%20HASAN%20ABDULLAH%20AL%20NASSER%20WATERMARK.pdf |
| _version_ | 1848888968005812224 |
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| author | Abdullah Al Nasser, Munef Hasan |
| author_facet | Abdullah Al Nasser, Munef Hasan |
| author_sort | Abdullah Al Nasser, Munef Hasan |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | Face recognition uses a variety of technologies and locations to carry out the attendance system. In order to recognise a face in real-time settings utilising a specific purpose device, attendance systems require accurate results. Video architecture is also achieved in our design by piercing the camera via a stoner- friendly interface. The Overeater (Histogram of Acquainted Grade) algorithm is used to recognise and segment the face from the VHS frame. Garbling a photo using the Overeater method to obtain a simplified interpretation of the image is the first phase, or pre-processing stage. Find the part of the image that most closely resembles a general Overeater encoding of a face using this simplified image. Also in the next step, figuring out the face's disguise by chancing the primary landmarks in the face. Once we've located those landmarks, we can utilise them to anchor the image such that the eyes and mouth are centred. Run the centred face image through a neural network that understands how to measure facial traits. Save those 128 measurements for later. Examine all of the faces we've measured in the past to find who has the most similar measurements to ours. That's the result of our match. Overall, we developed a Python programme that takes an image from a database and does all of the necessary changes for recognition, as well as checks the image in videos or in real time by accessing the camera using a Stoner-friendly interface. After a successful match is made, the name and time of the individual in attendance is recorded. |
| first_indexed | 2025-11-15T20:18:42Z |
| format | Thesis |
| id | uthm-6983 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English English English |
| last_indexed | 2025-11-15T20:18:42Z |
| publishDate | 2022 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-69832022-04-26T06:26:19Z http://eprints.uthm.edu.my/6983/ Face recognition employees attendance system Abdullah Al Nasser, Munef Hasan TA1501-1820 Applied optics. Photonics Face recognition uses a variety of technologies and locations to carry out the attendance system. In order to recognise a face in real-time settings utilising a specific purpose device, attendance systems require accurate results. Video architecture is also achieved in our design by piercing the camera via a stoner- friendly interface. The Overeater (Histogram of Acquainted Grade) algorithm is used to recognise and segment the face from the VHS frame. Garbling a photo using the Overeater method to obtain a simplified interpretation of the image is the first phase, or pre-processing stage. Find the part of the image that most closely resembles a general Overeater encoding of a face using this simplified image. Also in the next step, figuring out the face's disguise by chancing the primary landmarks in the face. Once we've located those landmarks, we can utilise them to anchor the image such that the eyes and mouth are centred. Run the centred face image through a neural network that understands how to measure facial traits. Save those 128 measurements for later. Examine all of the faces we've measured in the past to find who has the most similar measurements to ours. That's the result of our match. Overall, we developed a Python programme that takes an image from a database and does all of the necessary changes for recognition, as well as checks the image in videos or in real time by accessing the camera using a Stoner-friendly interface. After a successful match is made, the name and time of the individual in attendance is recorded. 2022-02 Thesis NonPeerReviewed text en http://eprints.uthm.edu.my/6983/1/24p%20MUNEF%20HASAN%20ABDULLAH%20AL%20NASSER.pdf text en http://eprints.uthm.edu.my/6983/2/MUNEF%20HASAN%20ABDULLAH%20AL%20NASSER%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/6983/3/MUNEF%20HASAN%20ABDULLAH%20AL%20NASSER%20WATERMARK.pdf Abdullah Al Nasser, Munef Hasan (2022) Face recognition employees attendance system. Masters thesis, Universiti Tun Hussein Malaysia. |
| spellingShingle | TA1501-1820 Applied optics. Photonics Abdullah Al Nasser, Munef Hasan Face recognition employees attendance system |
| title | Face recognition employees attendance system |
| title_full | Face recognition employees attendance system |
| title_fullStr | Face recognition employees attendance system |
| title_full_unstemmed | Face recognition employees attendance system |
| title_short | Face recognition employees attendance system |
| title_sort | face recognition employees attendance system |
| topic | TA1501-1820 Applied optics. Photonics |
| url | http://eprints.uthm.edu.my/6983/ http://eprints.uthm.edu.my/6983/1/24p%20MUNEF%20HASAN%20ABDULLAH%20AL%20NASSER.pdf http://eprints.uthm.edu.my/6983/2/MUNEF%20HASAN%20ABDULLAH%20AL%20NASSER%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/6983/3/MUNEF%20HASAN%20ABDULLAH%20AL%20NASSER%20WATERMARK.pdf |