Multi-stream gaussian mixture model based facial feature localization

This paper presents a new facial feature localization system which estimates positions of eyes, nose and mouth corners simultaneously. In contrast to conventional systems, we use the multi-stream Gaussian mixture model (GMM) framework in order to represent structural and appearance information of fa...

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Bibliographic Details
Published in2008 IEEE 16th Signal Processing, Communication and Applications Conference pp. 1 - 4
Main Authors Kumatani, Kenichi, Ekenel, Hazim K., Hua Gao, Stiefelhagen, Rainer, Ercil, Aytul
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2008
Subjects
Accuracy
Face
Facial features
Feature extraction
Mouth
Nose
Shape
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ISBN9781424419982
1424419980
ISSN2165-0608
DOI10.1109/SIU.2008.4632752

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Summary:This paper presents a new facial feature localization system which estimates positions of eyes, nose and mouth corners simultaneously. In contrast to conventional systems, we use the multi-stream Gaussian mixture model (GMM) framework in order to represent structural and appearance information of facial features. We construct a GMM for the region of each facial feature, where the principal component analysis is used to extract each facial feature. We also build a GMM which represents the structural information of a face, relative positions of facial features. Those models are combined based on the multi-stream framework. It can reduce the computation time to search region of interest (ROI). We demonstrate the effectiveness of our algorithm through experiments on the BioID Face Database.
ISBN:9781424419982
1424419980
ISSN:2165-0608
DOI:10.1109/SIU.2008.4632752