Classification-based face detection using compound features

Authors:
Linlin Huang;Akinobu Shimizu;Hidefumi Kobatake
Affiliations:
Graduate School of BASE, Tokyo University of Agriculture and Technology, Koganeishi, Tokyo, Japan;Graduate School of BASE, Tokyo University of Agriculture and Technology, Koganeishi, Tokyo, Japan;Graduate School of BASE, Tokyo University of Agriculture and Technology, Koganeishi, Tokyo, Japan
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Abstract

In this paper, we propose a classification-based face detection method using compound features. Four kinds of features, namely, intensity, Gabor filter feature, decomposed gradient feature, and Harr wavelet feature are combined to construct a compound feature vector. The projection of the feature vector on a reduced feature subspace learned by principal component analysis (PCA) is used as the input of the underlying classifier, which is a polynomial neural network (PNN). The experimental results on testing a large number of images demonstrate the effectiveness of the proposed method.