Facial Landmark Localization in Depth Images using Supervised Ridge Descent

Necati Cihan Camgoz, Vitomir Štruc, Berk Gokberk, Lale Akarun, Ahmet Alp Kindiroglu (2015): Facial Landmark Localization in Depth Images using Supervised Ridge Descent. Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW): Chaa Learn, 2015.

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

BibTeX (Download)

@conference{cihan2015facial,
title = {Facial Landmark Localization in Depth Images using Supervised Ridge Descent},
author = { Necati Cihan Camgoz and Vitomir \v{S}truc and Berk Gokberk and Lale Akarun and Ahmet Alp Kindiroglu},
url = {http://luks.fe.uni-lj.si/nluks/wp-content/uploads/2016/09/Camgoz_Facial_Landmark_Localization_ICCV_2015_paper.pdf},
year  = {2015},
date = {2015-01-01},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCVW): Chaa Learn},
pages = {136--141},
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},
keywords = {3d landmarking, facial landmarking, landmark localization, landmarking, ridge regression, SDM},
pubstate = {published},
tppubtype = {conference}
}