Facial recognition for partially occluded faces
Facial recognition is a highly developed method of determining a person's identity just by looking at an image of their face, and it has been used in a wide range of contexts. However, facial recognition models of previous researchers typically have trouble identifying faces behind masks, glass...
| Những tác giả chính: | , , , , , |
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| Định dạng: | Bài viết |
| Được phát hành: |
Institute of Advanced Engineering and Science (IAES)
2023
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| _version_ | 1825939545973063680 |
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| author | Naser, Omer Abdulhaleem Syed Ahmad, Sharifah Mumtazah Samsudin, Khairulmizam Hanafi, Marsyita Shafie, Siti Mariam Zamri, Nor Zarina |
| author_facet | Naser, Omer Abdulhaleem Syed Ahmad, Sharifah Mumtazah Samsudin, Khairulmizam Hanafi, Marsyita Shafie, Siti Mariam Zamri, Nor Zarina |
| author_sort | Naser, Omer Abdulhaleem |
| collection | UPM |
| description | Facial recognition is a highly developed method of determining a person's identity just by looking at an image of their face, and it has been used in a wide range of contexts. However, facial recognition models of previous researchers typically have trouble identifying faces behind masks, glasses, or other obstructions. Therefore, this paper aims to efficiently recognise faces obscured with masks and glasses. This research therefore proposes a method to solve the issue of partially obscured faces in facial recognition. The collected datasets for this study include CelebA, MFR2, WiderFace, LFW, and MegaFace Challenge datasets; all of these contain photos of occluded faces. This paper analyses masked facial images using multi-task cascaded convolutional neural networks (MTCNN). FaceNet adds more embeddings and verifications to face recognition. Support vector classification (SVC) labels the datasets to produce a reliable prediction probability. This study achieved around 99.50 accuracy for the training set and 95 for the testing set. This model recognizes partially obscured digital camera faces using the same datasets. We compare our results to comparable dataset studies to show how our method is more effective and accurate. |
| first_indexed | 2024-09-25T03:40:16Z |
| format | Article |
| id | upm.eprints-107928 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-09-25T03:40:16Z |
| publishDate | 2023 |
| publisher | Institute of Advanced Engineering and Science (IAES) |
| record_format | dspace |
| spelling | upm.eprints-1079282024-09-10T07:41:36Z http://psasir.upm.edu.my/id/eprint/107928/ Facial recognition for partially occluded faces Naser, Omer Abdulhaleem Syed Ahmad, Sharifah Mumtazah Samsudin, Khairulmizam Hanafi, Marsyita Shafie, Siti Mariam Zamri, Nor Zarina Facial recognition is a highly developed method of determining a person's identity just by looking at an image of their face, and it has been used in a wide range of contexts. However, facial recognition models of previous researchers typically have trouble identifying faces behind masks, glasses, or other obstructions. Therefore, this paper aims to efficiently recognise faces obscured with masks and glasses. This research therefore proposes a method to solve the issue of partially obscured faces in facial recognition. The collected datasets for this study include CelebA, MFR2, WiderFace, LFW, and MegaFace Challenge datasets; all of these contain photos of occluded faces. This paper analyses masked facial images using multi-task cascaded convolutional neural networks (MTCNN). FaceNet adds more embeddings and verifications to face recognition. Support vector classification (SVC) labels the datasets to produce a reliable prediction probability. This study achieved around 99.50 accuracy for the training set and 95 for the testing set. This model recognizes partially obscured digital camera faces using the same datasets. We compare our results to comparable dataset studies to show how our method is more effective and accurate. Institute of Advanced Engineering and Science (IAES) 2023 Article PeerReviewed Naser, Omer Abdulhaleem and Syed Ahmad, Sharifah Mumtazah and Samsudin, Khairulmizam and Hanafi, Marsyita and Shafie, Siti Mariam and Zamri, Nor Zarina (2023) Facial recognition for partially occluded faces. Indonesian Journal of Electrical Engineering and Computer Science, 30 (3). 1846 -1855. ISSN 2502-4752; ESSN: 2502-4760 https://ijeecs.iaescore.com/index.php/IJEECS/article/view/31073 10.11591/ijeecs.v30.i3.pp1846-1855 |
| spellingShingle | Naser, Omer Abdulhaleem Syed Ahmad, Sharifah Mumtazah Samsudin, Khairulmizam Hanafi, Marsyita Shafie, Siti Mariam Zamri, Nor Zarina Facial recognition for partially occluded faces |
| title | Facial recognition for partially occluded faces |
| title_full | Facial recognition for partially occluded faces |
| title_fullStr | Facial recognition for partially occluded faces |
| title_full_unstemmed | Facial recognition for partially occluded faces |
| title_short | Facial recognition for partially occluded faces |
| title_sort | facial recognition for partially occluded faces |
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