The DOI matches its Crossref record; 82 of the 82 references it cites were found in a registry or an index.
Face Alignment in Full Pose Range: A 3D Total Solution
Open accessAbstract
Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in the computer vision community. However, most algorithms are designed for faces in small to medium poses (yaw angle is smaller than 45 degree), which lack the ability to align faces in large poses up to 90 degree. The challenges are three-fold. First, the commonly used landmark face model assumes that all the landmarks are visible and is therefore not suitable for large poses. Second, the face appearance varies more drastically across large poses, from the frontal view to the profile view. Third, labelling landmarks in large poses is extremely challenging since the invisible landmarks have to be guessed. In this paper, we propose to tackle these three challenges in an new alignment framework termed 3D Dense Face Alignment (3DDFA), in which a dense 3D Morphable Model (3DMM) is fitted to the image via Cascaded Convolutional Neural Networks. We also utilize 3D information to synthesize face images in profile views to provide abundant samples for training. Experiments on the challenging AFLW database show that the proposed approach achieves significant improvements over the state-of-the-art methods.
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Object Detection with Discriminatively Trained Part-Based Models
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Self-citationFace recognition based on fitting a 3D morphable model
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Supervised Descent Method and Its Applications to Face Alignment
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Cited by
Towards Fast, Accurate and Stable 3D Dense Face Alignment
Guo, J., Zhu, X., Yang, Y., Yang, F., Lei, Z., & Li, S. Z. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58529-7_10 · Cited by 462
Self-citationRealistic Speech-Driven Facial Animation with GANs
Vougioukas, K., Petridis, S., & Pantic, M. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01251-8 · Cited by 298
Face Forgery Detection by 3D Decomposition
Zhu, X., Wang, H., Fei, H., Lei, Z., & Li, S. Z. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00295 · Cited by 106
Self-citationSemantic Alignment: Finding Semantically Consistent Ground-Truth for Facial Landmark Detection
Liu, Z., Zhu, X., Hu, G., Guo, H., Tang, M., Lei, Z., Robertson, N. M., & Wang, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00358 · Cited by 73
Self-citationFace Forgery Detection by 3D Decomposition and Composition Search
Zhu, X., Fei, H., Zhang, B., Zhang, T., Zhang, X., Li, S. Z., & Lei, Z. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3233586 · Cited by 41
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Metadata
- Authors
- Zhu, X., Liu, X., Lei, Z., & Li, S. Z.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2019-01-01
- Volume
- 41
- Issue
- 1
- Pages
- 78-92
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2265959009
- Cited by
- 391
- References
- 82
- FWCI
- 12.58
- Type
- journal article
- Language
- en
- Open access
- green
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