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 [BibTeX] [Marc21]
Leveraging Convolutional Pose Machines for Fast and Accurate Head Pose Estimation
Type of publication: Conference paper
Citation: Cao_IROS2018_2018
Publication status: Published
Booktitle: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2018
Month: October
Pages: 1089-1094
Publisher: IEEE
Location: Madrid, SPAIN
ISSN: 2153-0858
ISBN: 978-1-5386-8094-0
Abstract: We propose a head pose estimation framework that leverages on a recent keypoint detection model. More specifically, we apply the convolutional pose machines (CPMs) to input images, extract different types of facial keypoint features capturing appearance information and keypoint relationships, and train multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) for head pose estimation. The benefit of leveraging on the CPMs (which we apply anyway for other purposes like tracking) is that we can design highly efficient models for practical usage. We evaluate our approach on the Annotated Facial Landmarks in the Wild (AFLW) dataset and achieve competitive results with the state-of-the-art.
Projects Idiap
Authors Cao, Yuanzhouhan
Canévet, Olivier
Odobez, Jean-Marc
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Total mark: 0
  • Cao_IROS2018_2018.pdf