Multi-view facial landmark detection by using a 3D shape model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00243388" target="_blank" >RIV/68407700:21230/16:00243388 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.imavis.2015.11.003" target="_blank" >http://dx.doi.org/10.1016/j.imavis.2015.11.003</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.imavis.2015.11.003" target="_blank" >10.1016/j.imavis.2015.11.003</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-view facial landmark detection by using a 3D shape model

Popis výsledku v původním jazyce

An algorithm for accurate localization of facial landmarks coupled with a head pose estimation from a single monocular image is proposed. The algorithm is formulated as an optimization problem where the sum of individual landmark scoring functions is maximized with respect to the camera pose by fitting a parametric 3D shape model. The landmark scoring functions are trained by a structured output SVM classifier that takes a distance to the true landmark position into account when learning. The optimization criterion is non-convex and we propose a robust initialization scheme which employs a global method to detect a raw but reliable initial landmark position. Self-occlusions causing landmarks invisibility are handled explicitly by excluding the corresponding contributions from the data term. This allows the algorithm to operate correctly for a large range of viewing angles. Experiments on standard ``in-the-wild'' datasets demonstrate that the proposed algorithm outperforms several state-of-the-art landmark detectors especially for non-frontal face images. The algorithm achieves the average relative landmark localization error below 10% of the interocular distance in 98.3% of the 300W dataset test images.

Název v anglickém jazyce

Multi-view facial landmark detection by using a 3D shape model

Popis výsledku anglicky

An algorithm for accurate localization of facial landmarks coupled with a head pose estimation from a single monocular image is proposed. The algorithm is formulated as an optimization problem where the sum of individual landmark scoring functions is maximized with respect to the camera pose by fitting a parametric 3D shape model. The landmark scoring functions are trained by a structured output SVM classifier that takes a distance to the true landmark position into account when learning. The optimization criterion is non-convex and we propose a robust initialization scheme which employs a global method to detect a raw but reliable initial landmark position. Self-occlusions causing landmarks invisibility are handled explicitly by excluding the corresponding contributions from the data term. This allows the algorithm to operate correctly for a large range of viewing angles. Experiments on standard ``in-the-wild'' datasets demonstrate that the proposed algorithm outperforms several state-of-the-art landmark detectors especially for non-frontal face images. The algorithm achieves the average relative landmark localization error below 10% of the interocular distance in 98.3% of the 300W dataset test images.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Image and Vision Computing

ISSN

0262-8856

e-ISSN

—

Svazek periodika

47

Číslo periodika v rámci svazku

March

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

11

Strana od-do

60-70

Kód UT WoS článku

000377824500007

EID výsledku v databázi Scopus

2-s2.0-84952879775