A mini-review of face alignment - classical method versus deep learning approach / Nabilah Hamzah, Fadhlan Hafizhelmi Kamaru Zaman and Nooritawati Md Tahir
Face alignment is one of the vital research areas to be explored specifically face tasks like face recognition, face verification, face reconstruction, and facial expression analysis. Hence, the need for robust face alignment is still in demand. Numerous classic methods have used the 2D image for th...
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Universiti Teknologi MARA
2021
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| Online Access: | https://ir.uitm.edu.my/id/eprint/52049/1/52049.pdf https://ir.uitm.edu.my/id/eprint/52049/ https://jeesr.uitm.edu.my/ |
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| _version_ | 1839753444712251392 |
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| author | Hamzah, Nabilah Kamaru Zaman, Fadhlan Hafizhelmi Md Tahir, Nooritawati |
| author_facet | Hamzah, Nabilah Kamaru Zaman, Fadhlan Hafizhelmi Md Tahir, Nooritawati |
| author_sort | Hamzah, Nabilah |
| building | Tun Abdul Razak Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Mara |
| content_source | UiTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Face alignment is one of the vital research areas to be explored specifically face tasks like face recognition, face verification, face reconstruction, and facial expression analysis. Hence, the need for robust face alignment is still in demand. Numerous classic methods have used the 2D image for the detection of facial landmarks but this task is challenging due to several reasons, for instance, large poses, semi-frontal images, and facial expression. Abundant techniques have been established to mitigate all these challenges but there are far from being solved. Hence this mini-review discussed the face alignment methods based on the classic method to the state-of-the-art that includes the 2D-face alignment along with the 3D-face alignment approach. Based on the review done, the 3D model could combat large poses, facial expressions, and semi-frontal images however some of the facial landmarks are not visible and stack together for occluded face images. Hence, this will be the research area to be explored further in ensuring robustness and better accuracy in the face alignment area. |
| format | Article |
| id | my.uitm.ir-52049 |
| institution | Universiti Teknologi Mara |
| language | en |
| publishDate | 2021 |
| publisher | Universiti Teknologi MARA |
| record_format | eprints |
| spelling | my.uitm.ir-520492025-07-30T07:45:43Z https://ir.uitm.edu.my/id/eprint/52049/ A mini-review of face alignment - classical method versus deep learning approach / Nabilah Hamzah, Fadhlan Hafizhelmi Kamaru Zaman and Nooritawati Md Tahir jeesr Hamzah, Nabilah Kamaru Zaman, Fadhlan Hafizhelmi Md Tahir, Nooritawati Scanning systems Digital photography Scientific and technical applications Face alignment is one of the vital research areas to be explored specifically face tasks like face recognition, face verification, face reconstruction, and facial expression analysis. Hence, the need for robust face alignment is still in demand. Numerous classic methods have used the 2D image for the detection of facial landmarks but this task is challenging due to several reasons, for instance, large poses, semi-frontal images, and facial expression. Abundant techniques have been established to mitigate all these challenges but there are far from being solved. Hence this mini-review discussed the face alignment methods based on the classic method to the state-of-the-art that includes the 2D-face alignment along with the 3D-face alignment approach. Based on the review done, the 3D model could combat large poses, facial expressions, and semi-frontal images however some of the facial landmarks are not visible and stack together for occluded face images. Hence, this will be the research area to be explored further in ensuring robustness and better accuracy in the face alignment area. Universiti Teknologi MARA 2021-10 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/52049/1/52049.pdf Hamzah, Nabilah and Kamaru Zaman, Fadhlan Hafizhelmi and Md Tahir, Nooritawati (2021) A mini-review of face alignment - classical method versus deep learning approach / Nabilah Hamzah, Fadhlan Hafizhelmi Kamaru Zaman and Nooritawati Md Tahir. (2021) Journal of Electrical and Electronic Systems Research (JEESR) <https://ir.uitm.edu.my/view/publication/Journal_of_Electrical_and_Electronic_Systems_Research_=28JEESR=29.html>, 19 (1): 2. pp. 7-16. ISSN 1985-5389 https://jeesr.uitm.edu.my/ 10.24191/jeesr.v19i1.002 10.24191/jeesr.v19i1.002 10.24191/jeesr.v19i1.002 |
| spellingShingle | Scanning systems Digital photography Scientific and technical applications Hamzah, Nabilah Kamaru Zaman, Fadhlan Hafizhelmi Md Tahir, Nooritawati A mini-review of face alignment - classical method versus deep learning approach / Nabilah Hamzah, Fadhlan Hafizhelmi Kamaru Zaman and Nooritawati Md Tahir |
| title | A mini-review of face alignment - classical method versus deep learning approach / Nabilah Hamzah, Fadhlan Hafizhelmi Kamaru Zaman and Nooritawati Md Tahir |
| title_full | A mini-review of face alignment - classical method versus deep learning approach / Nabilah Hamzah, Fadhlan Hafizhelmi Kamaru Zaman and Nooritawati Md Tahir |
| title_fullStr | A mini-review of face alignment - classical method versus deep learning approach / Nabilah Hamzah, Fadhlan Hafizhelmi Kamaru Zaman and Nooritawati Md Tahir |
| title_full_unstemmed | A mini-review of face alignment - classical method versus deep learning approach / Nabilah Hamzah, Fadhlan Hafizhelmi Kamaru Zaman and Nooritawati Md Tahir |
| title_short | A mini-review of face alignment - classical method versus deep learning approach / Nabilah Hamzah, Fadhlan Hafizhelmi Kamaru Zaman and Nooritawati Md Tahir |
| title_sort | mini-review of face alignment - classical method versus deep learning approach / nabilah hamzah, fadhlan hafizhelmi kamaru zaman and nooritawati md tahir |
| topic | Scanning systems Digital photography Scientific and technical applications |
| url | https://ir.uitm.edu.my/id/eprint/52049/1/52049.pdf https://ir.uitm.edu.my/id/eprint/52049/ https://jeesr.uitm.edu.my/ |
| url_provider | http://ir.uitm.edu.my/ |