Facial Landmark Localization from 3D Images

Janez Križaj, Simon Dobrišek, France Mihelič, Vitomir Štruc (2016): Facial Landmark Localization from 3D Images. In: Proceedings of the Electrotechnical and Computer Science Conference (ERK), Portorož, Slovenia, 2016.

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.

BibTeX (Download)

@inproceedings{ERK2016Janez,
title = {Facial Landmark Localization from 3D Images},
author = {Janez Kri\v{z}aj and Simon Dobri\v{s}ek and France Miheli\v{c} and Vitomir \v{S}truc},
year  = {2016},
date = {2016-09-20},
booktitle = {Proceedings of the Electrotechnical and Computer Science Conference (ERK)},
address = {Portoro\v{z}, 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}
}