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Volumn , Issue , 2018, Pages 360-368

Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; OPTICAL FLOWS;

EID: 85055682668     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2018.00045     Document Type: Conference Paper
Times cited : (252)

References (41)
  • 1
    • 1542285823 scopus 로고    scopus 로고
    • Lucas-Kanade 20 years on: A unifying framework
    • 2, 3
    • S. Baker and I. Matthews. Lucas-Kanade 20 years on: A unifying framework. IJCV, 2004. 2, 3
    • (2004) IJCV
    • Baker, S.1    Matthews, I.2
  • 2
    • 84880827668 scopus 로고    scopus 로고
    • 3D shape regression for real-time facial animation
    • 1
    • C. Cao, Y. Weng, S. Lin, and K. Zhou. 3D shape regression for real-time facial animation. ACM Transactions on Graphics, 32(4):41, 2013. 1
    • (2013) ACM Transactions on Graphics , vol.32 , Issue.4 , pp. 41
    • Cao, C.1    Weng, Y.2    Lin, S.3    Zhou, K.4
  • 3
    • 84897113834 scopus 로고    scopus 로고
    • Face alignment by explicit shape regression
    • 2, 6
    • X. Cao, Y. Wei, F. Wen, and J. Sun. Face alignment by explicit shape regression. IJCV, 2014. 2, 6
    • (2014) IJCV
    • Cao, X.1    Wei, Y.2    Wen, F.3    Sun, J.4
  • 4
    • 85035235556 scopus 로고    scopus 로고
    • CLKN: Cascaded lucas-kanade networks for image alignment
    • 2, 3
    • C.-H. Chang, C.-N. Chou, and E. Y. Chang. CLKN: Cascaded lucas-kanade networks for image alignment. In CVPR, 2017. 2, 3
    • (2017) CVPR
    • Chang, C.-H.1    Chou, C.-N.2    Chang, E.Y.3
  • 7
    • 85035226122 scopus 로고    scopus 로고
    • More is less: A more complicated network with less inference complexity
    • 5
    • X. Dong, J. Huang, Y. Yang, and S. Yan. More is less: A more complicated network with less inference complexity. In CVPR, 2017. 5
    • (2017) CVPR
    • Dong, X.1    Huang, J.2    Yang, Y.3    Yan, S.4
  • 8
    • 85062867443 scopus 로고    scopus 로고
    • Style aggregated network for facial landmark detection
    • 2, 6
    • X. Dong, Y. Yan, W. Ouyang, and Y. Yang. Style aggregated network for facial landmark detection. In CVPR, 2018. 2, 6
    • (2018) CVPR
    • Dong, X.1    Yan, Y.2    Ouyang, W.3    Yang, Y.4
  • 10
    • 84986274465 scopus 로고    scopus 로고
    • Deep residual learning for image recognition
    • 5
    • K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016. 5
    • (2016) CVPR
    • He, K.1    Zhang, X.2    Ren, S.3    Sun, J.4
  • 12
    • 78149481816 scopus 로고    scopus 로고
    • Forward-backward error: Automatic detection of tracking failures
    • 2, 5
    • Z. Kalal, K. Mikolajczyk, and J. Matas. Forward-backward error: Automatic detection of tracking failures. In ICPR, 2010. 2, 5
    • (2010) ICPR
    • Kalal, Z.1    Mikolajczyk, K.2    Matas, J.3
  • 13
    • 85041904968 scopus 로고    scopus 로고
    • Synergy between face alignment and tracking via discriminative global consensus optimization
    • 1, 2, 5, 6
    • M. H. Khan, J. McDonagh, and G. Tzimiropoulos. Synergy between face alignment and tracking via discriminative global consensus optimization. In ICCV, 2017. 1, 2, 5, 6
    • (2017) ICCV
    • Khan, M.H.1    McDonagh, J.2    Tzimiropoulos, G.3
  • 14
    • 51949088884 scopus 로고    scopus 로고
    • Face tracking and recognition with visual constraints in real-world videos
    • 5
    • M. Kim, S. Kumar, V. Pavlovic, and H. Rowley. Face tracking and recognition with visual constraints in real-world videos. In CVPR, 2008. 5
    • (2008) CVPR
    • Kim, M.1    Kumar, S.2    Pavlovic, V.3    Rowley, H.4
  • 15
    • 84856655003 scopus 로고    scopus 로고
    • Annotated facial landmarks in the wild: A large-scale, realworld database for facial landmark localization
    • 1, 5
    • M. Koestinger, P. Wohlhart, P. M. Roth, and H. Bischof. Annotated facial landmarks in the wild: A large-scale, realworld database for facial landmark localization. In ICCV-W, 2011. 1, 5
    • (2011) ICCV-W
    • Koestinger, M.1    Wohlhart, P.2    Roth, P.M.3    Bischof, H.4
  • 16
    • 84876231242 scopus 로고    scopus 로고
    • ImageNet classification with deep convolutional neural networks
    • 2
    • A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, 2012. 2
    • (2012) NIPS
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 17
    • 85062889159 scopus 로고    scopus 로고
    • Two-stream transformer networks for video-based face alignment
    • 2, 6
    • H. Liu, J. Lu, J. Feng, and J. Zhou. Two-stream transformer networks for video-based face alignment. T-PAMI, 2017. 2, 6
    • (2017) T-PAMI
    • Liu, H.1    Lu, J.2    Feng, J.3    Zhou, J.4
  • 18
    • 0019647180 scopus 로고
    • An iterative image registration technique with an application to stereo vision
    • 2
    • B. D. Lucas, T. Kanade, et al. An iterative image registration technique with an application to stereo vision. In IJCAI, 1981. 2
    • (1981) IJCAI
    • Lucas, B.D.1    Kanade, T.2
  • 19
    • 85030213048 scopus 로고    scopus 로고
    • A deep regression architecture with two-stage reinitialization for high performance facial landmark detection
    • 1, 2, 4, 5, 6
    • J. Lv, X. Shao, J. Xing, C. Cheng, and X. Zhou. A deep regression architecture with two-stage reinitialization for high performance facial landmark detection. In CVPR, 2017. 1, 2, 4, 5, 6
    • (2017) CVPR
    • Lv, J.1    Shao, X.2    Xing, J.3    Cheng, C.4    Zhou, X.5
  • 20
    • 84990062418 scopus 로고    scopus 로고
    • Stacked hourglass networks for human pose estimation
    • 2, 4
    • A. Newell, K. Yang, and J. Deng. Stacked hourglass networks for human pose estimation. In ECCV, 2016. 2, 4
    • (2016) ECCV
    • Newell, A.1    Yang, K.2    Deng, J.3
  • 22
    • 85040145427 scopus 로고    scopus 로고
    • A recurrent encoder-decoder network for sequential face alignment
    • 1, 2
    • X. Peng, R. S. Feris, X. Wang, and D. N. Metaxas. A recurrent encoder-decoder network for sequential face alignment. In ECCV, 2016. 1, 2
    • (2016) ECCV
    • Peng, X.1    Feris, R.S.2    Wang, X.3    Metaxas, D.N.4
  • 23
    • 84973889176 scopus 로고    scopus 로고
    • PIEFA: Personalized incremental and ensemble face alignment
    • 1, 3, 5, 6
    • X. Peng, S. Zhang, Y. Yang, and D. N. Metaxas. PIEFA: Personalized incremental and ensemble face alignment. In ICCV, 2015. 1, 3, 5, 6
    • (2015) ICCV
    • Peng, X.1    Zhang, S.2    Yang, Y.3    Metaxas, D.N.4
  • 24
    • 85042259592 scopus 로고    scopus 로고
    • Toward personalized modeling: Incremental and ensemble alignment for sequential faces in the wild
    • 3
    • X. Peng, S. Zhang, Y. Yu, and D. N. Metaxas. Toward personalized modeling: Incremental and ensemble alignment for sequential faces in the wild. IJCV, 2017. 3
    • (2017) IJCV
    • Peng, X.1    Zhang, S.2    Yu, Y.3    Metaxas, D.N.4
  • 25
    • 84911442924 scopus 로고    scopus 로고
    • Face alignment at 3000 fps via regressing local binary features
    • 6
    • S. Ren, X. Cao, Y. Wei, and J. Sun. Face alignment at 3000 fps via regressing local binary features. In CVPR, 2014. 6
    • (2014) CVPR
    • Ren, S.1    Cao, X.2    Wei, Y.3    Sun, J.4
  • 27
    • 84911438905 scopus 로고    scopus 로고
    • RAPS: Robust and efficient automatic construction of person-specific deformable models
    • 3
    • C. Sagonas, Y. Panagakis, S. Zafeiriou, and M. Pantic. RAPS: Robust and efficient automatic construction of person-specific deformable models. In CVPR, 2014. 3
    • (2014) CVPR
    • Sagonas, C.1    Panagakis, Y.2    Zafeiriou, S.3    Pantic, M.4
  • 28
    • 84897520980 scopus 로고    scopus 로고
    • 300 faces in-The-wild challenge: The first facial landmark localization challenge
    • 1, 5, 6
    • C. Sagonas, G. Tzimiropoulos, S. Zafeiriou, and M. Pantic. 300 faces in-The-wild challenge: The first facial landmark localization challenge. In ICCV-W, 2013. 1, 5, 6
    • (2013) ICCV-W
    • Sagonas, C.1    Tzimiropoulos, G.2    Zafeiriou, S.3    Pantic, M.4
  • 29
    • 85026929789 scopus 로고    scopus 로고
    • Unsupervised video adaptation for parsing human motion
    • 3, 8
    • H. Shen, S.-I. Yu, Y. Yang, D. Meng, and A. Hauptmann. Unsupervised video adaptation for parsing human motion. In ECCV, 2014. 3, 8
    • (2014) ECCV
    • Shen, H.1    Yu, S.-I.2    Yang, Y.3    Meng, D.4    Hauptmann, A.5
  • 31
    • 85083953063 scopus 로고    scopus 로고
    • Very deep convolutional networks for large-scale image recognition
    • 5
    • K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In ICLR, 2015. 5
    • (2015) ICLR
    • Simonyan, K.1    Zisserman, A.2
  • 33
    • 84986309468 scopus 로고    scopus 로고
    • Mnemonic descent method: A recurrent process applied for end-to-end face alignment
    • 6
    • G. Trigeorgis, P. Snape, M. A. Nicolaou, E. Antonakos, and S. Zafeiriou. Mnemonic descent method: A recurrent process applied for end-to-end face alignment. In CVPR, 2016. 6
    • (2016) CVPR
    • Trigeorgis, G.1    Snape, P.2    Nicolaou, M.A.3    Antonakos, E.4    Zafeiriou, S.5
  • 34
    • 84957999859 scopus 로고    scopus 로고
    • Project-out cascaded regression with an application to face alignment
    • 5
    • G. Tzimiropoulos. Project-out cascaded regression with an application to face alignment. In CVPR, 2015. 5
    • (2015) CVPR
    • Tzimiropoulos, G.1
  • 36
    • 80052899838 scopus 로고    scopus 로고
    • Face recognition in unconstrained videos with matched background similarity
    • 5
    • L. Wolf, T. Hassner, and I. Maoz. Face recognition in unconstrained videos with matched background similarity. In CVPR, 2011. 5
    • (2011) CVPR
    • Wolf, L.1    Hassner, T.2    Maoz, I.3
  • 37
    • 84887383859 scopus 로고    scopus 로고
    • Supervised descent method and its applications to face alignment
    • 1, 2, 6
    • X. Xiong and F. De la Torre. Supervised descent method and its applications to face alignment. In CVPR, 2013. 1, 2, 6
    • (2013) CVPR
    • Xiong, X.1    De La Torre, F.2
  • 38
    • 85009935878 scopus 로고    scopus 로고
    • Facial landmark detection by deep multi-task learning
    • 2, 6
    • Z. Zhang, P. Luo, C. C. Loy, and X. Tang. Facial landmark detection by deep multi-task learning. In ECCV, 2014. 2, 6
    • (2014) ECCV
    • Zhang, Z.1    Luo, P.2    Loy, C.C.3    Tang, X.4
  • 39
    • 84959182922 scopus 로고    scopus 로고
    • Face alignment by coarse-to-fine shape searching
    • 5, 6
    • S. Zhu, C. Li, C. Change Loy, and X. Tang. Face alignment by coarse-to-fine shape searching. In CVPR, 2015. 5, 6
    • (2015) CVPR
    • Zhu, S.1    Li, C.2    Loy, C.C.3    Tang, X.4
  • 40
    • 84986243881 scopus 로고    scopus 로고
    • Unconstrained face alignment via cascaded compositional learning
    • 4, 5
    • S. Zhu, C. Li, C.-C. Loy, and X. Tang. Unconstrained face alignment via cascaded compositional learning. In CVPR, 2016. 4, 5
    • (2016) CVPR
    • Zhu, S.1    Li, C.2    Loy, C.-C.3    Tang, X.4
  • 41
    • 84876838667 scopus 로고    scopus 로고
    • Semi-supervised learning tutorial
    • 3
    • X. Zhu. Semi-supervised learning tutorial. In ICML, 2007. 3
    • (2007) ICML
    • Zhu, X.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.