Objave

@inproceedings{krivzaj2018localization,

title = {Localization of Facial Landmarks in Depth Images Using Gated Multiple Ridge Descent},

author = {Janez Križaj and Žiga Emeršič and Simon Dobrišek and Peter Peer and Vitomir Štruc},

url = {https://ieeexplore.ieee.org/abstract/document/8464215},

year  = {2018},

date = {2018-09-01},

booktitle = {2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)},

pages = {1--8},

organization = {IEEE},

abstract = {A novel method for automatic facial landmark localization is presented. The method builds on the supervised descent framework, which was shown to successfully localize landmarks in the presence of large expression variations and mild occlusions, but struggles when localizing landmarks on faces with large pose variations. We propose an extension of the supervised descent framework that trains multiple descent maps and results in increased robustness to pose variations. The performance of the proposed method is demonstrated on the Bosphorus, the FRGC and the UND data sets for the problem of facial landmark localization from 3D data. Our experimental results show that the proposed method exhibits increased robustness to pose variations, while retaining high performance in the case of expression and occlusion variations.},

keywords = {3d face, 3d landmarking, face alignment, face landmarking, gated ridge descent},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ERK2016Janez,

title = {Facial Landmark Localization from 3D Images},

author = {Janez Križaj and Simon Dobrišek and France Mihelič and Vitomir Štruc},

year  = {2016},

date = {2016-09-20},

booktitle = {Proceedings of the Electrotechnical and Computer Science Conference (ERK)},

address = {Portorož, Slovenia},

abstract = {A novel method for automatic facial landmark localization is presented. The method builds on the supervised descent framework, which was shown to successfully localize landmarks in the presence of large expression variations and mild occlusions, but struggles when localizing landmarks on faces with large pose variations. We propose an extension of the supervised descent framework which trains multiple descent maps and results in increased robustness to pose variations. The performance of the proposed method is demonstrated on the Bosphorus database for the problem of facial landmark localization from 3D data. Our experimental results show that the proposed method exhibits increased robustness to pose variations, while retaining high performance in the case of expression and occlusion variations.},

keywords = {3D face data, 3d landmarking, Bosphorus, face alignment, face image processing, facial landmarking, SDM, supervised descent framework},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cihan2015facial,

title = {Facial Landmark Localization in Depth Images using Supervised Ridge Descent},

author = {Necati Cihan Camgoz and Vitomir Štruc and Berk Gokberk and Lale Akarun and Ahmet Alp Kindiroglu},

url = {https://lmi.fe.uni-lj.si/en/faciallandmarklocalizationindepthimagesusingsupervisedridgedescent/},

year  = {2015},

date = {2015-01-01},

urldate = {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 = {inproceedings}

}