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Illumination invariant head pose estimation using random forests classifier and binary pattern run length matrix
- Illumination invariant head pose estimation using random forests classifier and binary pattern run length matrix
- Kim, Hyun Duk; Lee, Sang Heon; Sohn, Myoung Kyu; Kim, Dong Ju
- DGIST Authors
- Lee, Sang Heon; Sohn, Myoung Kyu
- Issue Date
- Human-centric Computing and Information Sciences, 4(1), 1-12
- Article Type
- Binary Pattern; Head Pose Estimation; Illumination-Invariant; Random Forests; Run Length Matrix
- 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.
- Springer Science + Business Media
- Related Researcher
- Convergence Research Center for Future Automotive Technology1. Journal Articles
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