Robust real-time face detection using hybrid neural networks

In this paper, a multi-stage face detection method using hybrid neural networks is presented. The method consists of three stages: preprocessing, feature extraction and pattern classification. We introduce an adaptive filtering technique which is based on a skin-color analysis using fuzzy min-max (FMM) neural networks. A modified convolutional neural network (CNN) is used to extract translation invariant feature maps for face detection. We present an extended version of fuzzy min-max (FMM) neural network which can be used not only for feature analysis but also for pattern classification. Two kinds of relevance factors between features and pattern classes are defined to analyze the saliency of features. These measures can be utilized to select more relevant features for the skin-color filtering process as well as the face detection process.
Publisher
SPRINGER-VERLAG BERLIN
Issue Date
2006
Language
ENG
Citation

COMPUTATIONAL INTELLIGENCE AND BIOINFORMATICS, PT 3, PROCEEDINGS BOOK SERIES: LECTURE NOTES IN COMPUTER SCIENCE, v.4115, pp.721 - 730

ISSN
0302-9743
URI
http://hdl.handle.net/10203/333
Appears in Collection
CS-Journal Papers(저널논문)
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