The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Face Alignment at 3000 FPS via Regressing Local Binary Features
Abstract
This paper presents a highly efficient, very accurate regression approach for face alignment. Our approach has two novel components: a set of local binary features, and a locality principle for learning those features. The locality principle guides us to learn a set of highly discriminative local binary features for each facial landmark independently. The obtained local binary features are used to jointly learn a linear regression for the final output. Our approach achieves the state-of-the-art results when tested on the current most challenging benchmarks. Furthermore, because extracting and regressing local binary features is computationally very cheap, our system is much faster than previous methods. It achieves over 3, 000 fps on a desktop or 300 fps on a mobile phone for locating a few dozens of landmarks.
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Cites
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Face detection, pose estimation, and landmark localization in the wild
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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
Active Appearance Models Revisited
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Cited by
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
Mollahosseini, A., Hasani, B., & Mahoor, M. H. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2740923 · Cited by 2,338
Ranjan, R., Patel, V. M., & Chellappa, R. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2781233 · Cited by 1,333
Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774
300 Faces In-The-Wild Challenge: Database and results
Sagonas, C., Antonakos, E., Tzimiropoulos, G., Zafeiriou, S., & Pantic, M. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.01.002 · Cited by 638
Fast Face-Swap Using Convolutional Neural Networks
Korshunova, I., Shi, W., Dambre, J., & Theis, L. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.397 · Cited by 501
Metadata
- Authors
- Ren, S., Cao, X., Wei, Y., & Sun, J.
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 1685-1692
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1998294030
- Cited by
- 853
- References
- 35
- FWCI
- 66.85
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Ren, S., Cao, X., Wei, Y., & Sun, J. (2014). Face Alignment at 3000 FPS via Regressing Local Binary Features. 2014 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2014.218
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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