A robust face detection method

Abstract

A new face detection method based on learning is proposed in this paper, it has three properties: first, it uses not only the local facial feature but also the global facial feature to design weak classifiers, a new kind of global facial feature called as the unified average face feature (UAFF) is proposed; second, it uses two kinds of rectangle feature as the local feature, different from other methods, these local features are selected and calculated only in the partial regions of face; third, these weak classifiers corresponding to the global facial features and the local facial features are combined and trained by our novel cascade classifier training algorithm to construct a cascade face detector. Because of these properties, our face detector is robust and generalizes well. Experimental results show that, with a small number of features, it can reach higher detection rate while maintain lower false alarm rate. Moreover, it can detect faces with partial occlusion.

DOI: 10.1109/ICIG.2004.23

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Cite this paper

@article{Su2004ARF, title={A robust face detection method}, author={Shiqian Su and Baocai Yin}, journal={Third International Conference on Image and Graphics (ICIG'04)}, year={2004}, pages={302-305} }