TR2020-067
LUVLi Face Alignment: Estimating Landmarks’ Location, Uncertainty, and Visibility Likelihood
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- "LUVLi Face Alignment: Estimating Landmarks’ Location, Uncertainty, and Visibility Likelihood", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), DOI: 10.1109/CVPR42600.2020.00826, June 2020.BibTeX TR2020-067 PDF Video Data Software
- @inproceedings{Kumar2020jun,
- author = {Kumar, Abhinav and Marks, Tim K. and Mou, Wenxuan and Wang, Ye and Cherian, Anoop and Jones, Michael J. and Liu, Xiaoming and Koike-Akino, Toshiaki and Feng, Chen},
- title = {LUVLi Face Alignment: Estimating Landmarks’ Location, Uncertainty, and Visibility Likelihood},
- booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
- year = 2020,
- month = jun,
- publisher = {IEEE},
- doi = {10.1109/CVPR42600.2020.00826},
- issn = {2575-7075},
- isbn = {978-1-7281-7168-5},
- url = {https://www.merl.com/publications/TR2020-067}
- }
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- "LUVLi Face Alignment: Estimating Landmarks’ Location, Uncertainty, and Visibility Likelihood", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), DOI: 10.1109/CVPR42600.2020.00826, June 2020.
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MERL Contacts:
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Research Areas:
Abstract:
Modern face alignment methods have become quite accurate at predicting the locations of facial landmarks, but they do not typically estimate the uncertainty of their predicted locations nor predict whether landmarks are visible. In this paper, we present a novel framework for jointly predicting landmark locations, associated uncertainties of these predicted locations, and landmark visibilities. We model these as mixed random variables and estimate them using a deep network trained with our proposed Location, Uncertainty, and Visibility Likelihood (LUVLi) loss. In addition, we release an entirely new labeling of a large face alignment dataset with over 19,000 face images in a full range of head poses. Each face is manually labeled with the ground-truth locations of 68 landmarks, with the additional information of whether each landmark is unoccluded, self-occluded (due to extreme head poses), or externally occluded. Not only does our joint estimation yield accurate estimates of the uncertainty of predicted landmark locations, but it also yields state-of-the-art estimates for the landmark locations themselves on multiple standard face alignment datasets. Our method’s estimates of the uncertainty of predicted landmark locations could be used to automatically identify input images on which face alignment fails, which can be critical for downstream tasks.
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Related News & Events
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NEWS MERL researchers presenting four papers and organizing two workshops at CVPR 2020 conference Date: June 14, 2020 - June 19, 2020
MERL Contacts: Anoop Cherian; Michael J. Jones; Toshiaki Koike-Akino; Tim K. Marks; Kuan-Chuan Peng; Ye Wang
Research Areas: Artificial Intelligence, Computer Vision, Machine LearningBrief- MERL researchers are presenting four papers (two oral papers and two posters) and organizing two workshops at the IEEE/CVF Computer Vision and Pattern Recognition (CVPR 2020) conference.
CVPR 2020 Orals with MERL authors:
1. "Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction," by Maosen Li, Siheng Chen, Yangheng Zhao, Ya Zhang, Yanfeng Wang, Qi Tian
2. "Collaborative Motion Prediction via Neural Motion Message Passing," by Yue Hu, Siheng Chen, Ya Zhang, Xiao Gu
CVPR 2020 Posters with MERL authors:
3. "LUVLi Face Alignment: Estimating Landmarks’ Location, Uncertainty, and Visibility Likelihood," by Abhinav Kumar, Tim K. Marks, Wenxuan Mou, Ye Wang, Michael Jones, Anoop Cherian, Toshiaki Koike-Akino, Xiaoming Liu, Chen Feng
4. "MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird’s Eye View Maps," by Pengxiang Wu, Siheng Chen, Dimitris N. Metaxas
CVPR 2020 Workshops co-organized by MERL researchers:
1. Fair, Data-Efficient and Trusted Computer Vision
2. Deep Declarative Networks.
- MERL researchers are presenting four papers (two oral papers and two posters) and organizing two workshops at the IEEE/CVF Computer Vision and Pattern Recognition (CVPR 2020) conference.
Related Video
Related Publication
- @article{Kumar2020apr,
- author = {Kumar, Abhinav and Marks, Tim K. and Mou, Wenxuan and Wang, Ye and Cherian, Anoop and Jones, Michael J. and Liu, Xiaoming and Koike-Akino, Toshiaki and Feng, Chen},
- title = {LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood},
- journal = {arXiv},
- year = 2020,
- month = apr,
- url = {https://arxiv.org/abs/2004.02980}
- }