The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Deep Alignment Network: A Convolutional Neural Network for Robust Face Alignment
Open accessAbstract
In this paper, we propose Deep Alignment Network (DAN), a robust face alignment method based on a deep neural network architecture. DAN consists of multiple stages, where each stage improves the locations of the facial landmarks estimated by the previous stage. Our method uses entire face images at all stages, contrary to the recently proposed face alignment methods that rely on local patches. This is possible thanks to the use of landmark heatmaps which provide visual information about landmark locations estimated at the previous stages of the algorithm. The use of entire face images rather than patches allows DAN to handle face images with large variation in head pose and difficult initializations. An extensive evaluation on two publicly available datasets shows that DAN reduces the state-of-the-art failure rate by up to 70%. Our method has also been submitted for evaluation as part of the Menpo challenge.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.220 · Cited by 6,542
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
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
Active Appearance Models Revisited
Matthews, I., & Baker, S. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029666.37597.d3 · Cited by 1,535
300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge
Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., & Pantic, M. (2013) · 2013 IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2013.59 · Cited by 1,119
Face Alignment at 3000 FPS via Regressing Local Binary Features
Ren, S., Cao, X., Wei, Y., & Sun, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.218 · Cited by 853
Face Alignment by Explicit Shape Regression
Cao, X., Wei, Y., Wen, F., & Sun, J. (2014) · International Journal of Computer Vision · doi:10.1007/s11263-013-0667-3 · Cited by 851
Cited by
Deep High-Resolution Representation Learning for Visual Recognition
Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., et al. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2983686 · Cited by 4,894
LUVLi Face Alignment: Estimating Landmarks’ Location, Uncertainty, and Visibility Likelihood
Kumar, A., Marks, T. K., Mou, W., Wang, Y., Jones, M., Cherian, A., Koike-Akino, T., Liu, X., & Feng, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00826 · Cited by 165
High-resolution neural face swapping for visual effects
Naruniec, J., Helminger, L., Schroers, C., & Weber, R. M. (2020) · Computer Graphics Forum · doi:10.1111/cgf.14062 · Cited by 154
Self-citationA review of deep learning‐based approaches for deepfake content detection
(2024) · doi:10.1111/exsy.13570 · Cited by 116
A Review of Deep Learning-based Approaches for Deepfake Content Detection
Passos, L. A., Jodas, D., Costa, K. A. P., Júnior, L. A. S., Rodrigues, D., Ser, J. D., Camacho*, D., & Papa, J. (2023) · doi:10.22541/au.169735672.27713914/v1 · Cited by 116
Metadata
- Authors
- Kowalski, M., Naruniec, J., & Trzcinski, T.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 2034-2043
- OpenAlex
- W2623892189
- Cited by
- 336
- References
- 24
- FWCI
- 12.30
- Type
- conference paper
- Language
- en
- Open access
- green
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- Kowalski, M., Naruniec, J., & Trzcinski, T. (2017). Deep Alignment Network: A Convolutional Neural Network for Robust Face Alignment. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/cvprw.2017.254
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