Direkt zum Inhalt springen
Image Understanding and Knowledge-Based Systems
TUM Department of Informatics
Technical University of Munich

Technical University of Munich

Menu

Links


Robustly Classifying Facial Components Using a Set of Adjusted Pixel Features (bibtex)
Robustly Classifying Facial Components Using a Set of Adjusted Pixel Features (bibtex)
by M Wimmer, C Mayer and B Radig
Abstract:
Efficient and accurate localization of the components of human faces, such as skin, lips, eyes, and brows, provides benefit to various real-world applications. However, high intra-class and small inter-class variations in color prevent simple but quick pixel classifiers from yielding robust results. In contrast, more elaborate classifiers consider shape or region features but they do not achieve real-time performance. In this paper, we show that it definitely is possible to robustly determine the facial components and achieve far more than real-time performance. We also use quick pixel-level classifiers and provide them with a set of pixel features that are adapted to the image characteristics beforehand. We do not manually select the pixel features and specify the calculation rules. In contrast, our idea is to provide a multitude of features and let the Machine Learning algorithm decide which of them are important. The evaluation draws a comparison to fixed approaches that do not adapt the computation of the features to the image content in any way. The obtained accuracy is precise enough to be used for real-world applications such as for model-based interpretation of human faces.
Reference:
Robustly Classifying Facial Components Using a Set of Adjusted Pixel Features (M Wimmer, C Mayer and B Radig), In Proc. of the International Conference on Face and Gesture Recognition (FGR08), 2008. 
Bibtex Entry:
@inproceedings{wimmer_robustly_2008,
 author = {M Wimmer and C Mayer and B Radig},
 title = {Robustly Classifying Facial Components Using a Set of Adjusted Pixel
	Features},
 booktitle = {Proc. of the International Conference on Face and Gesture Recognition
	({FGR08)}},
 year = {2008},
 address = {Amsterdam, Netherlands},
 month = {sep},
 abstract = {Efficient and accurate localization of the components of human faces,
	such as skin, lips, eyes, and brows, provides benefit to various
	real-world applications. However, high intra-class and small inter-class
	variations in color prevent simple but quick pixel classifiers from
	yielding robust results. In contrast, more elaborate classifiers
	consider shape or region features but they do not achieve real-time
	performance. In this paper, we show that it definitely is possible
	to robustly determine the facial components and achieve far more
	than real-time performance. We also use quick pixel-level classifiers
	and provide them with a set of pixel features that are adapted to
	the image characteristics beforehand. We do not manually select the
	pixel features and specify the calculation rules. In contrast, our
	idea is to provide a multitude of features and let the Machine Learning
	algorithm decide which of them are important. The evaluation draws
	a comparison to fixed approaches that do not adapt the computation
	of the features to the image content in any way. The obtained accuracy
	is precise enough to be used for real-world applications such as
	for model-based interpretation of human faces.},
 keywords = {facial expressions},
}
Powered by bibtexbrowser
Back to Projects

Rechte Seite

Informatik IX

Image Understanding and Knowledge-Based Systems

Boltzmannstrasse 3
85748 Garching

info@iuks.in.tum.de