Robust face detection using a modified radial basis function network

被引：0

作者：

Huang, LL ^{[1
]}

Shimizu, A ^{[1
]}

Hagihara, Y ^{[1
]}

Kobatake, H ^{[1
]}

机构：

[1] Tokyo Univ Agr & Technol, Grad Sch Bioapplicat & Syst Engn, Koganei, Tokyo 1848588, Japan

来源：

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2002年 / E85D卷 / 10期

关键词：

face detection; face recognition; PCA; radial basis function; neural network;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1654 / 1662

页数：9

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