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.
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Huang, L., Shimizu, A., Kobatake, H. (2005). Classification-Based Face Detection Using Compound Features. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_17
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DOI: https://doi.org/10.1007/11427445_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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