DocumentCode :
1574475
Title :
Estimation of the mouth features using deformable templates
Author :
Zhang, Liang
Author_Institution :
Inst. fur Theor. Nachrichtentech. und Inf., Hannover Univ., Germany
Volume :
3
fYear :
1997
Firstpage :
328
Abstract :
Automatic estimation of mouth features is one of the important topics for face recognition and model-based coding of videophone sequences. An automatic mouth feature estimation algorithm which uses deformable templates is developed. Here, the mouth features are represented by the corner points of the mouth as well as the so called lip outline parameters. The lip outline parameters describe the opening of the mouth and the thickness of the lips. Compared to previous works, simplified cost functions are introduced. Furthermore, an algorithm for automatic determination of whether the mouth is open or closed is developed. Experimental results obtained with typical videophone sequence are given to evaluate the performance of the proposed algorithm. It is shown that in about 37% of the images the mouth features could be automatically estimated, which can be used e.g. for improving face modelling in a model-based coder
Keywords :
edge detection; face recognition; feature extraction; image matching; image sequences; parameter estimation; video coding; videotelephony; automatic mouth feature estimation algorithm; closed mouth; corner points; cost functions; deformable templates; edge detector; experimental results; face modelling; face recognition; feature representation; lip outline parameters; lip thickness; model-based coder; model-based coding; mouth opening; performance evaluation; videophone sequence; videophone sequences; Cost function; Erbium; Face recognition; Facial features; Humans; Lips; Mouth; Parameter estimation; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing, 1997. Proceedings., International Conference on
Conference_Location :
Santa Barbara, CA
Print_ISBN :
0-8186-8183-7
Type :
conf
DOI :
10.1109/ICIP.1997.632107
Filename :
632107
Link To Document :
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