Abstract
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.
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Acknowledgments
The work of the second author was supported by the National Natural Science Foundation of China (NSFC) under Grant Number 60505006 and 60673110, Natural Science Foundation of Hei Long Jiang Province (F200512), Science and Technology Research Project of Educational Bureau of Hei Long Jiang Province (1151G033), the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, Postdoctoral Fund for Scientific Research of Hei Long Jiang Province (LHK-04093) and Science Fund of Hei Long Jiang University for Distinguished Young Scholars (JC200406).
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Wang, H., Li, P. & Zhang, T. Histogram feature-based Fisher linear discriminant for face detection. Neural Comput & Applic 17, 49–58 (2008). https://doi.org/10.1007/s00521-006-0081-7
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DOI: https://doi.org/10.1007/s00521-006-0081-7