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Human Head Pose Estimation on SASE Database Using Random Hough Regression Forests

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Video Analytics. Face and Facial Expression Recognition and Audience Measurement (VAAM 2016, FFER 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10165))

Abstract

In recent years head pose estimation has become an important task in face analysis scenarios. Given the availability of high resolution 3D sensors, the design of a high resolution head pose database would be beneficial for the community. In this paper, Random Hough Forests are used to estimate 3D head pose and location on a new 3D head database, SASE, which represents the baseline performance on the new data for an upcoming international head pose estimation competition. The data in SASE is acquired with a Microsoft Kinect 2 camera, including the RGB and depth information of 50 subjects with a large sample of head poses, allowing us to test methods for real-life scenarios. We briefly review the database while showing baseline head pose estimation results based on Random Hough Forests.

The original version of this chapter was revised: The spelling of the second author’s name was corrected. The erratum to this chapter is available at DOI: 10.1007/978-3-319-56687-0_14

An erratum to this chapter can be found at http://dx.doi.org/10.1007/978-3-319-56687-0_14

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Acknowledgement

This work has been partially supported by the Estonian Research Grant (PUT638), Spanish projects TIN2013-43478-P and TIN2016-74946-P, the European Commission Horizon 2020 granted project SEE.4C under call H2020-ICT-2015, and the Estonian Centre of Excellence in IT (EXCITE) funded by the European Regional Development Fund.

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Authors and Affiliations

  1. ICV Research Group, Institute of Technology, University of Tartu, Tartu, Estonia

    Iiris Lüsi & Gholamreza Anbarjafari

  2. Computer Vision Center, Universitat de Barcelona, Barcelona, Spain

    Sergio Escalera

Authors
  1. Iiris Lüsi
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  2. Sergio Escalera
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  3. Gholamreza Anbarjafari
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Corresponding author

Correspondence to Iiris Lüsi .

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Editors and Affiliations

  1. Aalborg University, Aalborg, Denmark

    Kamal Nasrollahi

  2. Institute of Applied Sciences and Intelligent Systems, Lecce, Italy

    Cosimo Distante

  3. Stevens Institute of Technology, Hoboken, New Jersey, USA

    Gang Hua

  4. Queen Mary University of London, London, United Kingdom

    Andrea Cavallaro

  5. Aalborg University, Aalborg, Denmark

    Thomas B. Moeslund

  6. Università di Catania, Catania, Italy

    Sebastiano Battiato

  7. Rensselaer Polytechnic Institute, Troy, New York, USA

    Qiang Ji

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Lüsi, I., Escalera, S., Anbarjafari, G. (2017). Human Head Pose Estimation on SASE Database Using Random Hough Regression Forests. In: Nasrollahi, K., et al. Video Analytics. Face and Facial Expression Recognition and Audience Measurement. VAAM FFER 2016 2016. Lecture Notes in Computer Science(), vol 10165. Springer, Cham. https://doi.org/10.1007/978-3-319-56687-0_12

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  • DOI: https://doi.org/10.1007/978-3-319-56687-0_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56686-3

  • Online ISBN: 978-3-319-56687-0

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