Facial Landmark Localization in Depth Images Using Supervised Ridge Descent

Necati Cihan Camgöz, Vitomir Struc, Berk Gökberk, Lale Akarun, Ahmet Alp Kindiroglu

ICCVW 2015 pp. 378-383

doi:10.1109/ICCVW.2015.57 /iccvw/2015/camgoz2015iccvw-facial/

Abstract

Supervised Descent Method (SDM) has proven successful in many computer vision applications such as face alignment, tracking and camera calibration. Recent studies which used SDM, achieved state of the-art performance on facial landmark localization in depth images [4]. In this study, we propose to use ridge regression instead of least squares regression for learning the SDM, and to change feature sizes in each iteration, effectively turning the landmark search into a coarse to fine process. We apply the proposed method to facial landmark localization on the Bosphorus 3D Face Database, using frontal depth images with no occlusion. Experimental results confirm that both ridge regression and using adaptive feature sizes improve the localization accuracy considerably.

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Text

Camgöz et al. "Facial Landmark Localization in Depth Images Using Supervised Ridge Descent." IEEE/CVF International Conference on Computer Vision Workshops, 2015. doi:10.1109/ICCVW.2015.57

Markdown

[Camgöz et al. "Facial Landmark Localization in Depth Images Using Supervised Ridge Descent." IEEE/CVF International Conference on Computer Vision Workshops, 2015.](https://mlanthology.org/iccvw/2015/camgoz2015iccvw-facial/) doi:10.1109/ICCVW.2015.57

BibTeX

@inproceedings{camgoz2015iccvw-facial,
  title     = {{Facial Landmark Localization in Depth Images Using Supervised Ridge Descent}},
  author    = {Camgöz, Necati Cihan and Struc, Vitomir and Gökberk, Berk and Akarun, Lale and Kindiroglu, Ahmet Alp},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2015},
  pages     = {378-383},
  doi       = {10.1109/ICCVW.2015.57},
  url       = {https://mlanthology.org/iccvw/2015/camgoz2015iccvw-facial/}
}