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Facial landmark detection is an important yet challenging task for real-world computer vision applications.
T. Pfister, J. Charles, and A. Zisserman, “Flowing convnets for human pose estimation in videos,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 1913–1921
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M. Valstar, B. Martinez, X. Binefa, and M. Pantic, “Facial point detection using boosted regression and graph models,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 2010, pp. 2729–2736
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2011
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M. Köstinger, P. Wohlhart, P. M. Roth, and H. Bischof, “Annotated facial landmarks in the wild: A large-scale, real-world database for facial landmark localization,” in Proceedings of the IEEE International Conference on Computer Vision Workshops . IEEE, 2011, pp. 2144–2151
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X. Zhu and D. Ramanan, “Face detection, pose estimation, and landmark localization in the wild,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2012, pp. 2879–2886
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V. Le, J. Brandt, Z. Lin, L. Bourdev, and T. S. Huang, “Interactive facial feature localization,” in European Conference on Computer Vision , 2012, pp. 679–692
2012
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X. Xiong and F. De la Torre, “Supervised descent method and its applications to face alignment,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2013, pp. 532–539
2013
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G. Tzimiropoulos and M. Pantic, “Optimization problems for fast aam fitting in-the-wild,” in Proceedings of the IEEE International Conference on Computer Vision , 2013, pp. 593–600
2013
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Y. Sun, X. Wang, and X. Tang, “Deep convolutional network cascade for facial point detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2013, pp. 3476–3483
2013
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C. Sagonas, G. Tzimiropoulos, S. Zafeiriou, and M. Pantic, “300 faces in-the-wild challenge: The first facial landmark localization challenge,” in Proceedings of the IEEE International Conference on Computer Vision Workshops , 2013, pp. 397–403
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X. P. Burgos-Artizzu, P. Perona, and P. Dollár, “Robust face landmark estimation under occlusion,” in Proceedings of the IEEE International Conference on Computer Vision , 2013, pp. 1513–1520
2013
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A. Asthana, S. Zafeiriou, S. Cheng, and M. Pantic, “Robust discriminative response map fitting with constrained local models,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2013, pp. 3444–3451
2013
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T. Baltrusaitis, P. Robinson, and L.-P. Morency, “Constrained local neural fields for robust facial landmark detection in the wild,” in Proceedings of the IEEE International Conference on Computer Vision Workshops , 2013, pp. 354–361
2013
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P. N. Belhumeur, D. W. Jacobs, D. J. Kriegman, and N. Kumar, “Localizing parts of faces using a consensus of exemplars,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 35, no. 12, pp. 2930–2940, 2013
2013
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X. Cao, Y. Wei, F. Wen, and J. Sun, “Face alignment by explicit shape regression,” International Journal of Computer Vision , vol. 107, no. 2, pp. 177–190, 2014
2014
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S. Ren, X. Cao, Y. Wei, and J. Sun, “Face alignment at 3000 fps via regressing local binary features,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2014, pp. 1685–1692
2014
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J. Zhang, S. Shan, M. Kan, and X. Chen, “Coarse-to-fine auto-encoder networks (cfan) for real-time face alignment,” in European Conference on Computer Vision , 2014, pp. 1–16
2014
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G. Tzimiropoulos and M. Pantic, “Gauss-newton deformable part models for face alignment in-the-wild,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2014, pp. 1851–1858
2014
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J. J. Tompson, A. Jain, Y. LeCun, and C. Bregler, “Joint training of a convolutional network and a graphical model for human pose estimation,” in Advances in neural information processing systems , 2014, pp. 1799–1807
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Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” in Proceedings of the 22nd ACM international conference on Multimedia . ACM, 2014, pp. 675–678
2014
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A. Newell, K. Yang, and J. Deng, “Stacked hourglass networks for human pose estimation,” in European Conference on Computer Vision . Springer, 2016, pp. 483–499
2016
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F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” in Proceedings of the International Conference on Learning Representations , 2016
2016
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——, “Large-pose face alignment via cnn-based dense 3d model fitting,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 4188–4196
2016
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S. Zhu, C. Li, C.-C. Loy, and X. Tang, “Unconstrained face alignment via cascaded compositional learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 3409–3417
2016
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J. Alabort-i Medina, E. Antonakos, J. Booth, P. Snape, and S. Zafeiriou, “Menpo: A comprehensive platform for parametric image alignment and visual deformable models,” in Proceedings of the 22nd ACM international conference on Multimedia . ACM, 2014, pp. 679–682
2014
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V. Kazemi and J. Sullivan, “One millisecond face alignment with an ensemble of regression trees,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2014, pp. 1867–1874
2014
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B. M. Smith, J. Brandt, Z. Lin, and L. Zhang, “Nonparametric context modeling of local appearance for pose-and expression-robust facial landmark localization,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2014, pp. 1741–1748
2014
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G. Ghiasi and C. C. Fowlkes, “Occlusion coherence: Localizing occluded faces with a hierarchical deformable part model,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2014, pp. 2385–2392
2014
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S. Zhu, C. Li, C. Change Loy, and X. Tang, “Face alignment by coarse-to-fine shape searching,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 4998–5006
2015
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J. Alabort-i Medina and S. Zafeiriou, “Unifying holistic and parts-based deformable model fitting,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 3679–3688
2015
Cited alongside, same era.
2015
Cited alongside, same era.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 1–9
2015
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2016
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X. Yu, F. Zhou, and M. Chandraker, “Deep deformation network for object landmark localization,” in European Conference on Computer Vision . Springer, 2016, pp. 52–70
2016
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Q. Li, Z. Sun, and R. He, “Fast multi-view face alignment via multi-task auto-encoders,” in International Joint Conference on Biometrics , 2017, pp. 538–545
2017
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A. Bulat and G. Tzimiropoulos, “How far are we from solving the 2d & 3d face alignment problem? (and a dataset of 230,000 3d facial landmarks),” in Proceedings of the IEEE International Conference on Computer Vision , Oct 2017, pp. 1021–1030
2017
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J. Lv, X. Shao, J. Xing, C. Cheng, and X. Zhou, “A deep regression architecture with two-stage re-initialization for high performance facial landmark detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 3691–3700
2017
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X. Xu and I. A. Kakadiaris, “Joint head pose estimation and face alignment framework using global and local cnn features,” in 12th IEEE International Conference on Automatic Face Gesture Recognition , vol. 2, 2017, pp. 642–649
2017
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A. Kumar, A. Alavi, and R. Chellappa, “Kepler: Keypoint and pose estimation of unconstrained faces by learning efficient h-cnn regressors,” in 12th IEEE International Conference on Automatic Face Gesture Recognition , 2017, pp. 258–265
2017
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Y. Liu, A. Jourabloo, W. Ren, and X. Liu, “Dense face alignment,” in Proceedings of the IEEE International Conference on Computer Vision Workshops , 2017
2017
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A. Jourabloo, M. Ye, X. Liu, and L. Ren, “Pose-invariant face alignment with a single cnn,” in Proceedings of the IEEE International Conference on Computer Vision , Oct 2017, pp. 3219–3228
2017
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S. Xiao, J. Feng, L. Liu, X. Nie, W. Wang, S. Yan, and A. Kassim, “Recurrent 3d-2d dual learning for large-pose facial landmark detection,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 1642–1651
2017
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R. Ranjan, V. M. Patel, and R. Chellappa, “Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017
2017
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C. Bhagavatula, C. Zhu, K. Luu, and M. Savvides, “Faster than real-time facial alignment: A 3d spatial transformer network approach in unconstrained poses,” in Proceedings of the IEEE International Conference on Computer Vision , Oct 2017, pp. 4000–4009
2017
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Y. Wu, C. Gou, and Q. Ji, “Simultaneous facial landmark detection, pose and deformation estimation under facial occlusion,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , July 2017, pp. 5719–5728
2017
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J. Yang, Q. Liu, and K. Zhang, “Stacked hourglass network for robust facial landmark localisation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops . IEEE, 2017, pp. 2025–2033
2033
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M. Kowalski, J. Naruniec, and T. Trzcinski, “Deep alignment network: A convolutional neural network for robust face alignment,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , July 2017, pp. 2034–2043
2043
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A. Zadeh, Y. C. Lim, T. Baltrušaitis, and L.-P. Morency, “Convolutional experts constrained local model for 3d facial landmark detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2017, pp. 2051–2059
2059
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