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Head pose estimation based on random forests with binary pattern run length matrix

Title
Head pose estimation based on random forests with binary pattern run length matrix
Authors
Kim, HyundukLee, Sang-HeonSohn, Myoung-KyuKim, Dong-JuRyu, Nuri
DGIST Authors
Lee, Sang-HeonSohn, Myoung-Kyu
Issue Date
2014
Citation
5th FTRA International Conference on Computer Science and its Applications, CSA 2013, 279 LNEE, 255-260
Type
Conference
Article Type
Conference Paper
ISBN
9780000000000
ISSN
1876-1100
Abstract
In this paper, a novel approach for head pose estimation in gray-level images is presented. In the proposed algorithm, there were two techniques 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 combined a Local Binary Pattern and a Run Length matrix. Experimental results show that our algorithm is robust against illumination change. © 2014 Springer-Verlag Berlin Heidelberg.
URI
http://hdl.handle.net/20.500.11750/5496
DOI
10.1007/978-3-642-41674-3_37
Publisher
Springer Verlag
Related Researcher
Files:
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Collection:
Convergence Research Center for Future Automotive Technology2. Conference Papers


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