Face detection has been considered one of the most important areas of research in computer vision due to its wide range of use in human face-related applications. This paper addresses the problem of face detection using Hough transform employed within the random forests framework. The proposed Hough forests-based method is a task-adapted codebooks of local facial appearance with a randomized selection of features at each split that allow fast supervised training and fast matching at test time, where the codebooks are built upon a pool of heterogeneous local appearance features and the codebook is learned for the face appearance features that models the spatial distribution and appearance of facial parts of the human face. Experimental results are included to verify the effectiveness and feasibility of the proposed method.


Random Forest Gaussian Mixture Model Face Detection Probabilistic Vote Binary Test 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • M. Hassaballah
    • 1
  • Mourad Ahmed
    • 1
  1. 1.Department of Mathematics, Faculty of ScienceSouth Valley UniversityQenaEgypt

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