The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Pengesanan muka daripada imej bersepah sangat mencabar kerana pelbagai jenis wajah dan kerumitan latar belakang imej. Dalam makalah ini, kami mencadangkan pendekatan berasaskan rangkaian saraf untuk mencari pandangan hadapan muka manusia dalam imej yang bersepah. Kami menggunakan rangkaian fungsi asas jejari (RBFN) untuk pemisahan corak muka dan bukan muka, dan kerumitan RBFN dikurangkan dengan analisis komponen utama (PCA). Pengaruh bilangan unit tersembunyi dan konfigurasi fungsi asas pada prestasi pengesanan telah disiasat. Untuk meningkatkan lagi prestasi, kami menyepadukan jarak dari subruang ciri ke dalam RBFN. Kaedah yang dicadangkan telah mencapai kadar pengesanan yang tinggi dan kadar positif palsu yang rendah pada ujian sejumlah besar imej.
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Salinan
LinLin HUANG, Akinobu SHIMIZU, Yoshihiro HAGIHARA, Hidefumi KOBATAKE, "Robust Face Detection Using a Modified Radial Basis Function Network" in IEICE TRANSACTIONS on Information,
vol. E85-D, no. 10, pp. 1654-1662, October 2002, doi: .
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/e85-d_10_1654/_p
Salinan
@ARTICLE{e85-d_10_1654,
author={LinLin HUANG, Akinobu SHIMIZU, Yoshihiro HAGIHARA, Hidefumi KOBATAKE, },
journal={IEICE TRANSACTIONS on Information},
title={Robust Face Detection Using a Modified Radial Basis Function Network},
year={2002},
volume={E85-D},
number={10},
pages={1654-1662},
abstract={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.},
keywords={},
doi={},
ISSN={},
month={October},}
Salinan
TY - JOUR
TI - Robust Face Detection Using a Modified Radial Basis Function Network
T2 - IEICE TRANSACTIONS on Information
SP - 1654
EP - 1662
AU - LinLin HUANG
AU - Akinobu SHIMIZU
AU - Yoshihiro HAGIHARA
AU - Hidefumi KOBATAKE
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E85-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2002
AB - 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.
ER -