Modification of the AdaBoost-based Detector for Partially Occluded Faces

  • Authors:

  • Jie Chen;Shiguang Shan;Shengye Yang;Xilin Chen;Wen Gao

  • Affiliations:

  • Harbin Institute of Technology, Harbin, 150001, China;Chinese Academy of Sciences, Beijing 100080, China;Chinese Academy of Sciences, Beijing 100080, China;Chinese Academy of Sciences, Beijing 100080, China;Harbin Institute of Technology, Harbin, 150001, China

  • Venue:
  • ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
  • Year:
  • 2006

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Abstract

While face detection seems a solved problem under general conditions, most state-of-the-art systems degrade rapidly when faces are partially occluded by other objects. This paper presents a solution to detect partially occluded faces by reasonably modifying the AdaBoost-based face detector. Our basic idea is that the weak classifiers in the AdaBoost-based face detector, each corresponding to a Haar-like feature, are inherently a patch-based model. Therefore, one can divide the whole face region into multiple patches, and map those weak classifiers to the patches. The weak classifiers belonging to each patch are re-formed to be a new classifier to determine if it is a valid face patch-without occlusion. Finally, we combine all of the valid face patches by assigning the patches with different weights to make the final decision whether the input subwindow is a face. The experimental results show that the proposed method is promising for the detection of occluded faces.