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@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}

}

@inproceedings{BioID_Struc_2011,

title = {Principal directions of synthetic exact filters for robust real-time eye localization},

author = {Vitomir Štruc and Jerneja Žganec-Gros and Nikola Pavešić},

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

doi = {10.1007/978-3-642-19530-3_17},

year  = {2011},

date = {2011-01-01},

urldate = {2011-01-01},

booktitle = {Proceedings of the COST workshop on Biometrics and Identity Management (BioID)},

volume = {6583/2011},

pages = {180/192},

publisher = {Springer-Verlag},

address = {Berlin, Heidelberg},

series = {Lecture Notes on Computer Science},

abstract = {The alignment of the facial region with a predefined canonical form is one of the most crucial steps in a face recognition system. Most of the existing alignment techniques rely on the position of the eyes and, hence, require an efficient and reliable eye localization procedure. In this paper we propose a novel technique for this purpose, which exploits a new class of correlation filters called Principal directions of Synthetic Exact Filters (PSEFs). The proposed filters represent a generalization of the recently proposed Average of Synthetic Exact Filters (ASEFs) and exhibit desirable properties, such as relatively short training times, computational simplicity, high localization rates and real time capabilities. We present the theory of PSEF filter construction, elaborate on their characteristics and finally develop an efficient procedure for eye localization using several PSEF filters. We demonstrate the effectiveness of the proposed class of correlation filters for the task of eye localization on facial images from the FERET database and show that for the tested task they outperform the established Haar cascade object detector as well as the ASEF correlation filters.},

keywords = {ASEF, correlation filters, eye localization, face image processing, landmark localization, landmarking, PSEF},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DOGS_Struc_2010,

title = {Eye Localization using correlation filters},

author = {Vitomir Štruc and Jerneja Žganec-Gros and Nikola Pavešić},

year  = {2010},

date = {2010-01-01},

booktitle = {Proceedings of the International Conference DOGS},

pages = {188-191},

address = {Novi Sad, Serbia},

keywords = {ASEF, correlation filters, eye localization, face image processing, landmark localization, PSEF},

pubstate = {published},

tppubtype = {inproceedings}

}