AMDO 2016: Articulated Motion and Deformable Objects pp 24-33 | Cite as
Head-Pose Estimation In-the-Wild Using a Random Forest
Conference paper
First Online:
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.
Keywords
Head-pose estimation Random forest Real-time In-the-wildNotes
Acknowledgements
The authors gratefully acknowledge funding from the Spanish Ministry of Economy and Competitiveness under project SPACES-UPM (TIN2013-47630-C2-2R).
References
- 1.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)CrossRefGoogle Scholar
- 2.Balasubramanian, V., Ye, J., Panchanathan, S.: Biased manifold embedding: a framework for person-independent head pose estimation (2007)Google Scholar
- 3.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)CrossRefGoogle Scholar
- 4.Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)MathSciNetCrossRefMATHGoogle Scholar
- 5.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)Google Scholar
- 6.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)Google Scholar
- 7.Dollar, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: Proceedings on British Machine Vision Conference (BMVC) (2009)Google Scholar
- 8.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)CrossRefGoogle Scholar
- 9.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)Google Scholar
- 10.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)Google Scholar
- 11.Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-PIE (2008)Google Scholar
- 12.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)Google Scholar
- 13.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)Google Scholar
- 14.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)Google Scholar
- 15.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)CrossRefGoogle Scholar
- 16.Stiefelhagen, R.: Estimating head pose with neural networks. In: Proceedings of International Conference on Pattern Recognition Workshops (ICPRW) (2004)Google Scholar
- 17.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)Google Scholar
- 18.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)Google Scholar
- 19.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)MathSciNetCrossRefGoogle Scholar
- 20.Wu, J., Trivedi, M.M.: A two-stage head pose estimation framework and evaluation. Pattern Recognit. 41(3), 1138–1158 (2008)CrossRefMATHGoogle Scholar
- 21.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)Google Scholar
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