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Combining random forest with multi-block local binary pattern feature selection for multiclass head pose estimation

Title
Combining random forest with multi-block local binary pattern feature selection for multiclass head pose estimation
Authors
Kang M.-J.Lee J.-K.Kang J.-W.
Ewha Authors
강제원
SCOPUS Author ID
강제원scopus
Issue Date
2017
Journal Title
PLoS ONE
ISSN
1932-6203JCR Link
Citation
vol. 12, no. 7
Publisher
Public Library of Science
Indexed
SCIE; SCOPUS scopus
Abstract
A new head pose estimation technique based on Random Forest (RF) and texture features for facial image analysis using a monocular camera is proposed in this paper, especially about how to efficiently combine the random forest and the features. In the proposed technique a randomized tree with useful attributes is trained to improve estimation accuracy and tolerance of occlusions and illumination. Specifically, a number of features including Multi-scale Block Local Block Pattern (MB-LBP) are extracted from an image, and random features such as the MB-LBP scale parameters, a block coordinate, and a layer of an image pyramid in the feature pool are used for training the tree. The randomized tree aims to maximize the information gain at each node while random samples traverse the nodes in the tree. To this aim, a split function considering the uniform property of the LBP feature is developed to move sample blocks to the left or the right children nodes. The trees are independently trained with random inputs, yet they are grouped to form a random forest so that the results collected from the trees are used for make the final decision. Precisely, we use a Maximum-A-Posteriori criterion in the decision. It is demonstrated with experimental results that the proposed technique provides significantly enhanced classification performance in the head pose estimation in various conditions of illumination, poses, expressions, and facial occlusions. © 2017 Kang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
DOI
10.1371/journal.pone.0180792
Appears in Collections:
엘텍공과대학 > 전자공학과 > Journal papers
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