Landmark Localization in Occluded Faces Using Deep Learning Approach

1. Wu, Y., Shah, S.K., Kakadiaris, I.A.: GoDP: globally optimized dual pathway deep network architecture for facial landmark localization in-the-wild. Image Vis. Comput. 73, 1–6 (2018)

   Article  Google Scholar 

2. Feng, Z.-H., et al.: Wing loss for robust facial landmark localisation with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)

   Google Scholar 

3. Dong, X., et al.: Style aggregated network for facial landmark detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)

   Google Scholar 

4. Wang, N., et al.: Facial feature point detection: a comprehensive survey. Neurocomputing 275, 50–65 (2018)

   Article  Google Scholar 

5. Wu, Y., Ji, Q.: Facial landmark detection: a literature survey. Int. J. Comput. Vis. 127(2), 115–142 (2019)

   Article  Google Scholar 

6. Zhu, M., et al.: Robust facial landmark detection via occlusion-adaptive deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)

   Google Scholar 

7. Fan, H., Zhou, E.: Approaching human level facial landmark localization by deep learning. Image Vis. Comput. 47, 27–35 (2016)

   Article  Google Scholar 

8. Zhang, Z., et al.: Facial landmark detection by deep multi-task learning. In: European Conference on Computer Vision. Springer, Cham (2014)

   Google Scholar 

9. Bargal, S.A., et al.: Emotion recognition in the wild from videos using images. In: Proceedings of the 18th ACM International Conference on Multimodal Interaction (2016)

   Google Scholar 

10. Ansari, A.-N., Abdel-Mottaleb, M.: 3D face modeling using two views and a generic face model with application to 3D face recognition. In: Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003. IEEE (2003)

    Google Scholar 

11. Ranjan, R., Patel, V.M., Chellappa, R.: Hyperface: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Trans. Pattern Anal. Mach. Intell. 41(1), 121–135 (2017)

    Article  Google Scholar 

12. Alashbi, A.A.S., Sunar, M.S.: Occluded face detection, face in Niqab dataset. In: International Conference of Reliable Information and Communication Technology. Springer, Cham (2019)

    Google Scholar 

13. Wang, C., et al.: A novel coronavirus outbreak of global health concern. The Lancet 395(10223), 470–473 (2020)

    Article  Google Scholar 

14. Kowalski, M.: Localization and tracking of facial landmarks in images and video sequences. The Institute of Radioelectronics and Multimedia Technology (2018)

    Google Scholar 

15. Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: European Conference on Computer Vision. Springer, Heidelberg (1998)

    Google Scholar 

16. Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 681–685 (2001)

    Article  Google Scholar 

17. Milborrow, S., Nicolls, F.: Locating facial features with an extended active shape model. In: European Conference on Computer Vision. Springer, Heidelberg (2008)

    Google Scholar 

18. Cao, X., et al.: Face alignment by explicit shape regression. Int. J. Comput. Vis. 107(2), 177–190 (2014)

    Article  MathSciNet  Google Scholar 

19. Wu, Y., Wang, Z., Ji, Q.: Facial feature tracking under varying facial expressions and face poses based on restricted Boltzmann machines. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2013)

    Google Scholar 

20. Wu, Y., Wang, Z., Ji, Q.: A hierarchical probabilistic model for facial feature detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014)

    Google Scholar 

21. Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2013)

    Google Scholar 

22. Zhang, Z., et al.: Learning deep representation for face alignment with auxiliary attributes. IEEE Trans. Pattern Anal. Mach. Intell. 38(5), 918–930 (2015)

    Article  Google Scholar 

23. Zhou, E., et al.: Extensive facial landmark localization with coarse-to-fine convolutional network cascade. In: Proceedings of the IEEE International Conference on Computer Vision Workshops (2013)

    Google Scholar 

24. Xia, Y., Zhang, B., Coenen, F.: Face occlusion detection using deep convolutional neural networks. Int. J. Pattern Recognit. Artif. Intell. 30(09), 1660010 (2016)

    Article  Google Scholar 

25. Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (2015)

    Google Scholar 

26. Rothe, R., Guillaumin, M., Van Gool, L.: Non-maximum suppression for object detection by passing messages between windows. In: Asian Conference on Computer Vision. Springer, Cham (2014)

    Google Scholar 

27. Koestinger, M., et al.: Annotated facial landmarks in the wild: a large-scale, real-world database for facial landmark localization. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops). IEEE (2011)

    Google Scholar 

28. Le, V., et al.: Interactive facial feature localization. In: European Conference on Computer Vision. Springer, Heidelberg (2012)

    Google Scholar 

29. Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2012)

    Google Scholar 

30. Sagonas, C., et al.: A semi-automatic methodology for facial landmark annotation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2013)

    Google Scholar 

31. Zhang, K., et al.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016)

    Article  Google Scholar 

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