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

Title
Illumination invariant head pose estimation using random forests classifier and binary pattern run length matrix
Authors
Kim, Hyun DukLee, Sang HeonSohn, Myoung KyuKim, Dong Ju
DGIST Authors
Lee, Sang HeonSohn, Myoung Kyu
Issue Date
2014
Citation
Human-centric Computing and Information Sciences, 4(1), 1-12
Type
Article
Article Type
Article
Keywords
Binary PatternHead Pose EstimationIllumination-InvariantRandom ForestsRun Length Matrix
ISSN
2192-1962
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. © 2014, Kim et al.; licensee Springer.
URI
http://hdl.handle.net/20.500.11750/5277
DOI
10.1186/s13673-014-0009-7
Publisher
springer berlin
Related Researcher
Files:
There are no files associated with this item.
Collection:
Convergence Research Center for Future Automotive Technology1. Journal Articles


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