The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Face alignment by Explicit Shape Regression
Abstract
We present a very efficient, highly accurate, “Explicit Shape Regression” approach for face alignment. Unlike previous regression-based approaches, we directly learn a vectorial regression function to infer the whole facial shape (a set of facial landmarks) from the image and explicitly minimize the alignment errors over the training data. The inherent shape constraint is naturally encoded into the regressor in a cascaded learning framework and applied from coarse to fine during the test, without using a fixed parametric shape model as in most previous methods. To make the regression more effective and efficient, we design a two-level boosted regression, shape-indexed features and a correlation-based feature selection method. This combination enables us to learn accurate models from large training data in a short time (20 minutes for 2,000 training images), and run regression extremely fast in test (15 ms for a 87 landmarks shape). Experiments on challenging data show that our approach significantly outperforms the state-of-the-art in terms of both accuracy and efficiency.
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Cites
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Cootes, T. F., Edwards, G. J., & Taylor, C. J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.927467 · Cited by 5,409
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
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Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments
Huang, G. B., Mattar, M., Berg, T. L., & Miller, E. G. (2008) · HAL · Cited by 4,445
Real-time human pose recognition in parts from single depth images
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Supervised Descent Method and Its Applications to Face Alignment
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Cited by
Cascade R-CNN: Delving Into High Quality Object Detection
Cai, Z., & Vasconcelos, N. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00644 · Cited by 7,370
One millisecond face alignment with an ensemble of regression trees
Kazemi, V., & Sullivan, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.241 · Cited by 2,831
Supervised Descent Method and Its Applications to Face Alignment
Xiong, X., & De la Torre, F. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.75 · Cited by 1,896
Deep Convolutional Network Cascade for Facial Point Detection
Sun, Y., Wang, X., & Tang, X. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.446 · Cited by 1,382
Facial Landmark Detection by Deep Multi-task Learning
Zhang, Z., Luo, P., Loy, C. C., & Tang, X. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10599-4_7 · Cited by 1,365
Metadata
- Authors
- Cao, X., Wei, Y., Wen, F., & Sun, J.
- Venue
- 2012 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2012-06
- Pages
- 2887-2894
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2138406903
- Cited by
- 364
- References
- 29
- FWCI
- 37.61
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Cao, X., Wei, Y., Wen, F., & Sun, J. (2012). Face alignment by Explicit Shape Regression. 2012 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2012.6248015
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
