Illumination invariant head pose estimation using random forests classifier and binary pattern run length matrix View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-12

AUTHORS

Hyunduk Kim, Sang-Heon Lee, Myoung-Kyu Sohn, Dong-Ju Kim

ABSTRACT

In this paper, a novel approach for head pose estimation in gray-level images is presented. In the proposed algorithm, two techniques were employed. In order to deal with the large set of training data, the method of Random Forests was employed; this is a state-of-the-art classification algorithm in the field of computer vision. In order to make this system robust in terms of illumination, a Binary Pattern Run Length matrix was employed; this matrix is combination of Binary Pattern and a Run Length matrix. The binary pattern was calculated by randomly selected operator. In order to extract feature of training patch, we calculate statistical texture features from the Binary Pattern Run Length matrix. Moreover we perform some techniques to real-time operation, such as control the number of binary test. Experimental results show that our algorithm is efficient and robust against illumination change. More... »

PAGES

9

References to SciGraph publications

  • 2006. Synergistic Face Detection and Pose Estimation with Energy-Based Models in TOWARD CATEGORY-LEVEL OBJECT RECOGNITION
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13673-014-0009-7

    DOI

    http://dx.doi.org/10.1186/s13673-014-0009-7

    DIMENSIONS

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