Face and Landmark Detection by Using Cascade of Classifiers

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00212536" target="_blank" >RIV/68407700:21230/13:00212536 - isvavai.cz</a>

Výsledek na webu

<a href="http://cmp.felk.cvut.cz/pub/cmp/articles/franc/Cevilkalp-FaceDetector-FG2013.pdf" target="_blank" >http://cmp.felk.cvut.cz/pub/cmp/articles/franc/Cevilkalp-FaceDetector-FG2013.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/FG.2013.6553705" target="_blank" >10.1109/FG.2013.6553705</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Face and Landmark Detection by Using Cascade of Classifiers

Popis výsledku v původním jazyce

In this paper, we consider face detection along with facial landmark localization inspired by the recent studies showing that incorporating object parts improves the detection accuracy. To this end, we train roots and parts detectors where the roots detector returns candidate image regions that cover the entire face, and the parts detector searches for the landmark locations within the candidate region. We use a cascade of binary and one-class type classifiers for the roots detection and SVM like learning algorithm for the parts detection. Our proposed face detector outperforms the most of the successful face detection algorithms in the literature and gives the second best result on all tested challenging face detection databases. Experimental resultsshow that including parts improves the detection performance when face images are large and the details of eyes and mouth are clearly visible, but does not introduce any improvement when the images are small.

Název v anglickém jazyce

Face and Landmark Detection by Using Cascade of Classifiers

Popis výsledku anglicky

In this paper, we consider face detection along with facial landmark localization inspired by the recent studies showing that incorporating object parts improves the detection accuracy. To this end, we train roots and parts detectors where the roots detector returns candidate image regions that cover the entire face, and the parts detector searches for the landmark locations within the candidate region. We use a cascade of binary and one-class type classifiers for the roots detection and SVM like learning algorithm for the parts detection. Our proposed face detector outperforms the most of the successful face detection algorithms in the literature and gives the second best result on all tested challenging face detection databases. Experimental resultsshow that including parts improves the detection performance when face images are large and the details of eyes and mouth are clearly visible, but does not introduce any improvement when the images are small.

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/7E10047" target="_blank" >7E10047: Humanoids with auditory and visual abilities in populated spaces</a><br>

Návaznosti

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

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

Automatic Face and Gesture Recognition (FG), 2013 10th IEEE International Conference and Workshops on

ISBN

978-1-4673-5545-2

ISSN

—

e-ISSN

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Počet stran výsledku

7

Strana od-do

1-7

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Shanghai

Datum konání akce

22. 4. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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