Face Detection Using Principal Component Analysis and Polynomial Neural Network
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概要
- 論文の詳細を見る
In this paper, we present a new face detection method based on principal component analysis(PCA)technique and polynomial neural network. PCA is applied to reduce the dimensionality of a feature vector for face detection. The polynomial neural network which has been trained with both face and non-face feature vectors examines small windows on an input image and decides whether each window contains a face or not. A bootstrap algorithm is used to collect negative samples, which allows adding false detections into the training set. The experiment shows that the system performs well in terms of detection rate and false rate.
- 社団法人電子情報通信学会の論文
- 2001-01-27
著者
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Kobatake H
Tokyo Univ. Agriculture & Technol. Tokyo Jpn
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Hagihara Y
Tokyo University Of Agriculture & Technology
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SHIMIZU Akinobu
Graduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Tec
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KOBATAKE Hidefumi
Graduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Tec
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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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Huang Lin-Lin
Graduate School of Bio-Applications and Systems Engineering, Tokyo University of Agri.& Tech.
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Kobatake Hidefumi
Graduate School Of Bio-applications And Systems Engineering Tokyo Univ. Of Agriculture & Technol
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Huang Lin-lin
Graduate School Of Bio-applications And Systems Engineering Tokyo University Of Agri.& Tech.
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Hagihara Yoshihiro
Graduate School Of Bio-applications And Systems Engineering Tokyo University Of Agriculture & Te
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