Robust Face Detection Using a Modified Radial Basis Function Network
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概要
- 論文の詳細を見る
Face detection from cluttered images is very challenging due to the wide variety of faces and the complexity of image backgrounds. In this paper, we propose a neural network based approach for locating frontal views of human faces in cluttered images. We use a radial basis function network (RBFN) for separation of face and non-face patterns, and the complexity of RBFN is reduced by principal component analysis (PCA). The influence of the number of hidden units and the configuration of basis functions on the detection performance was investigated. To further improve the performance, we integrate the distance from feature subspace into the RBFN. The proposed method has achieved high detection rate and low false positive rate on testing a large number of images.
- 社団法人電子情報通信学会の論文
- 2002-10-01
著者
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Shimizu Akinobu
Graduate School Of Base Tokyo University Of Agri. & Tech.
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HAGIHARA Yoshihiro
Graduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture & Techn
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Kobatake Hidefumi
Graduate School Of Bio-applications And Systems Engineering Tokyo Univ. Of Agriculture & Technol
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Huang Linlin
Graduate School Of Base Tokyo University Of Agri. & Tech.
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Huang Linlin
Graduate School Of Bio-applications And Systems Engineering Tokyo University Of Agriculture & Te
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Hagihara Yoshihiro
Graduate School Of Bio-applications And Systems Engineering Tokyo University Of Agriculture & Te
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