Deep learning for real world face alignment
Face alignment is one of the fundamental steps in a vast number of tasks of high economical and social value, ranging from security to health and entertainment. Despite the attention received from the community for more than 2 decades and the success of cascaded regression based approaches, many cha...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
| Published: |
2019
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| Online Access: | https://eprints.nottingham.ac.uk/56145/ |
| _version_ | 1848799278788509696 |
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| author | Bulat, Adrian |
| author_facet | Bulat, Adrian |
| author_sort | Bulat, Adrian |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Face alignment is one of the fundamental steps in a vast number of tasks of high economical and social value, ranging from security to health and entertainment. Despite the attention received from the community for more than 2 decades and the success of cascaded regression based approaches, many challenges were yet to be solved, such as the case of near-profile poses and low resolution faces.
In this thesis, we successfully address a series of such challenges in the area of face alignment and super-resolution, significantly pushing the state-of-the-art by proposing novel deep learning-based architectures specially tailored for fine grained recognition tasks. In summary, we address the following problems: (I) fitting faces found in large poses (Chapter 3), (II) in both 2D and 3D space (Chapter 4), creating in the process (III) the largest in-the-wild large pose 3D face alignment dataset (Chapter 4). While the case of high resolution faces was actively explored in the past, in this thesis we systematically study and address a new challenge: that of (IV) fitting landmarks in very low resolution faces (Chapter 6). While deep learning based approaches achieved remarkable results on a wide variety of tasks, they are usually slow having high computational requirements. As such, in Chapter 5, we propose (V) a novel residual block carefully crafted for binarized neural networks that significantly improves the speed, due to the use of binary operations for both the weights and the activations, while maintaining a similar or competitive accuracy.
The results presented through out this thesis set the new state-of-the-art on both 2D & 3D face alignment and face super-resolution. |
| first_indexed | 2025-11-14T20:33:08Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-56145 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:33:08Z |
| publishDate | 2019 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-561452025-02-28T12:10:57Z https://eprints.nottingham.ac.uk/56145/ Deep learning for real world face alignment Bulat, Adrian Face alignment is one of the fundamental steps in a vast number of tasks of high economical and social value, ranging from security to health and entertainment. Despite the attention received from the community for more than 2 decades and the success of cascaded regression based approaches, many challenges were yet to be solved, such as the case of near-profile poses and low resolution faces. In this thesis, we successfully address a series of such challenges in the area of face alignment and super-resolution, significantly pushing the state-of-the-art by proposing novel deep learning-based architectures specially tailored for fine grained recognition tasks. In summary, we address the following problems: (I) fitting faces found in large poses (Chapter 3), (II) in both 2D and 3D space (Chapter 4), creating in the process (III) the largest in-the-wild large pose 3D face alignment dataset (Chapter 4). While the case of high resolution faces was actively explored in the past, in this thesis we systematically study and address a new challenge: that of (IV) fitting landmarks in very low resolution faces (Chapter 6). While deep learning based approaches achieved remarkable results on a wide variety of tasks, they are usually slow having high computational requirements. As such, in Chapter 5, we propose (V) a novel residual block carefully crafted for binarized neural networks that significantly improves the speed, due to the use of binary operations for both the weights and the activations, while maintaining a similar or competitive accuracy. The results presented through out this thesis set the new state-of-the-art on both 2D & 3D face alignment and face super-resolution. 2019-07-24 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en arr https://eprints.nottingham.ac.uk/56145/1/thesis_ab.pdf Bulat, Adrian (2019) Deep learning for real world face alignment. PhD thesis, University of Nottingham. computer vision face alignment |
| spellingShingle | computer vision face alignment Bulat, Adrian Deep learning for real world face alignment |
| title | Deep learning for real world face alignment |
| title_full | Deep learning for real world face alignment |
| title_fullStr | Deep learning for real world face alignment |
| title_full_unstemmed | Deep learning for real world face alignment |
| title_short | Deep learning for real world face alignment |
| title_sort | deep learning for real world face alignment |
| topic | computer vision face alignment |
| url | https://eprints.nottingham.ac.uk/56145/ |