Robust Face Detection Using a Modified Radial Basis Function Network

LinLin HUANG  Akinobu SHIMIZU  Yoshihiro HAGIHARA  Hidefumi KOBATAKE  

IEICE TRANSACTIONS on Information and Systems   Vol.E85-D   No.10   pp.1654-1662
Publication Date: 2002/10/01
Online ISSN: 
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Image Processing, Image Pattern Recognition
face detection,  face recognition,  PCA,  radial basis function,  neural network,  

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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.