Abstract
Human head-pose estimation has attracted a lot of interest because it is the first step of most face analysis tasks. However, many of the existing approaches address this problem in laboratory conditions. In this paper, we present a real-time algorithm that estimates the head-pose from unrestricted 2D gray-scale images. We propose a classification scheme, based on a Random Forest, where patches extracted randomly from the image cast votes for the corresponding discrete head-pose angle. In the experiments, the algorithm performs similar and better than the state-of-the-art in controlled and in-the-wild databases respectively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ba, S.O., Odobez, J.M.: Multiperson visual focus of attention from head pose and meeting contextual cues. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 101–116 (2011)
Balasubramanian, V., Ye, J., Panchanathan, S.: Biased manifold embedding: a framework for person-independent head pose estimation (2007)
BenAbdelkader, C.: Robust head pose estimation using supervised manifold learning. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 518–531. Springer, Heidelberg (2010)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Criminisi, A., Shotton, J., Konukoglu, E.: Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning. Tech. Rep. MSR-TR-2011-114, Microsoft Research (2011)
Dantone, M., Gall, J., Fanelli, G., Gool, L.V.: Real-time facial feature detection using conditional regression forests. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR) (2012)
Dollar, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: Proceedings on British Machine Vision Conference (BMVC) (2009)
Fanelli, G., Dantone, M., Gall, J., Fossati, A., Van Gool, L.: Random forests for real time 3D face analysis. Int. J. Comput. Vis. 101(3), 437–458 (2013)
Gaschler, A., Jentzsch, S., Giuliani, M., Huth, K., de Ruiter, J., Knoll, A.: Social behavior recognition using body posture and head pose for human-robot interaction. In: Proceedings of the International Conference on Intelligent Robots and Systems (IROS) (2012)
Geng, X., Xia, Y.: Head pose estimation based on multivariate label distribution. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR) (2014)
Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-PIE (2008)
Haj, M.A., González, J., Davis, L.S.: On partial least squares in head pose estimation: how to simultaneously deal with misalignment. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR) (2012)
Hara, K., Chellappa, R.: Growing regression forests by classification: applications to object pose estimation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part II. LNCS, vol. 8690, pp. 552–567. Springer, Heidelberg (2014)
Marín-Jiménez, M.J., Ferrari, V., Zisserman, A.: Here’s looking at you, kid: detecting people looking at each other in videos. In: Proceedings on British Machine Vision Conference (BMVC) (2011)
Murphy-Chutorian, E., Trivedi, M.M.: Head pose estimation in computer vision: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 31(4), 607–626 (2009)
Stiefelhagen, R.: Estimating head pose with neural networks. In: Proceedings of International Conference on Pattern Recognition Workshops (ICPRW) (2004)
Subramanian, R., Yan, R.Y., Staiano, J., Lanz, O., Sebe, N.: On the relationship between head pose, social attention and personality prediction for unstructured and dynamic group interactions. In: Proceedings of International Conference on Multimodal Interaction (2013)
Sundararajan, K., Woodard, D.L.: Head pose estimation in the wild using approximate view manifolds. In: Proceedings of Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2015)
Valenti, R., Sebe, N., Gevers, T.: Combining head pose and eye location information for gaze estimation. IEEE Trans. Image Process. 21(2), 802–815 (2012)
Wu, J., Trivedi, M.M.: A two-stage head pose estimation framework and evaluation. Pattern Recognit. 41(3), 1138–1158 (2008)
Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR) (2012)
Acknowledgements
The authors gratefully acknowledge funding from the Spanish Ministry of Economy and Competitiveness under project SPACES-UPM (TIN2013-47630-C2-2R).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Valle, R., Buenaposada, J.M., Valdés, A., Baumela, L. (2016). Head-Pose Estimation In-the-Wild Using a Random Forest. In: Perales, F., Kittler, J. (eds) Articulated Motion and Deformable Objects. AMDO 2016. Lecture Notes in Computer Science(), vol 9756. Springer, Cham. https://doi.org/10.1007/978-3-319-41778-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-41778-3_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41777-6
Online ISBN: 978-3-319-41778-3
eBook Packages: Computer ScienceComputer Science (R0)