Proposal of novel histogram features for face detection

Authors:
Haijing Haijing;Peihua Li;Tianwen Zhang
Affiliations:
Harbin Institute of Technology, School of Computer Science and Technology, Harbin, Heilongjiang, China;College of Computer Science and Technology, Heilongjiang University, China;Harbin Institute of Technology, School of Computer Science and Technology, Harbin, Heilongjiang, China
Venue:
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Year:
2005

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Abstract

This paper presents novel features for face detection in the paradigm of AdaBoost algorithm. Features are multi-dimensional histograms computed from a set of rectangles in the filtered images, and they represent marginal distributions of these rectangles. The filter banks consist of intensity, Laplacian of Gaussian (Difference of Gaussians), and Gabor filters, aiming at capturing spatial and frequency properties of human faces at different scales and different orientations. The best features selected by AdaBoost, pairs of filter and rectangle, can thus be interpreted as boosted marginal distributions of human faces. The result of preliminary experiments demonstrate that the selected features are much more powerful to describe the face pattern than the simple features of Viola and Jones and some variants which can only capture several moments of ONE dimensional histogram in intensity images.