A Regularized Framework for Feature Selection in Face Detection and Authentication
International Journal of Computer Vision
Entropic selection of histogram features for efficient classification
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Expert Systems with Applications: An International Journal
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The face pattern is described by pairs of template-based histogram and Fisher projection orientation under the framework of AdaBoost learning in this paper. We assume that a set of templates are available first. To avoid making strong assumptions about distributional structure while still retaining good properties for estimation, the classical statistical model, histogram, is used to summarize the response of each template. By introducing a novel “Integral Histogram Image”, we can compute histogram rapidly. Then, we turn to Fisher linear discriminant for each template to project histogram from d-dimensional subspace to one-dimensional subspace. Best features, used to describe face pattern, are selected by AdaBoost learning. The results of experiments demonstrate that the selected features are much more powerful to represent the face pattern than the simple rectangle features used by Viola and Jones and some variants.