Abstract
This paper describes a Hough Forest based approach for fast head pose estimation in RGB images. The system has been designed for Human-Computer Interaction (HCI), in a way that with just a simple web-cam, our solution is able to detect the head and simultaneously estimate its pose. We leverage the Hough Forest with Probabilistic Locally Enhanced Voting model, and integrate it into a system with a skin detection step and a tracking filter for the head orientation. Our implementation drastically speeds up the head pose estimations, improving their accuracy with respect to the original model. We present extensive experiments on a publicly available and challenging dataset, where our approach outperforms the state-of-the-art.
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
References
Schulter, S., Leistner, C., Wohlhart, P., Roth, P.M., Bischof, H.: Alternating regression forests for object detection and pose estimation. In: CVPR (2013)
Jones, M., Viola, P.: Fast multi-view face detection. Technical report (2003)
Morency, L.P., Sundberg, P., Darrell, T.: Pose estimation using 3D view-based eigenspaces. In: AMFG (2003)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. PAMI 23(6), 681–685 (2001)
Cootes, T.F., Wheeler, G.V., Walker, K.N., Taylor, C.J.: View-based active appearance models. In: AMFG (2000)
Ramnath, K., Koterba, S., Xiao, J., Hu, C., Matthews, I., Baker, S., Cohn, J., Kanade, T.: Multi-view aam fitting and construction. IJCV 76(2), 183–204 (2008)
Storer, M., Urschler, M., Bischof, H.: 3d-mam: 3d morphable appearance model for efficient fine head pose estimation from still images. In: Workshop on Subspace Methods (2009)
Breitenstein, M.D., Kuettel, D., Weise, T., Van Gool, L.J., Pfister, H.: Real-time face pose estimation from single range images. In: CVPR (2008)
Ding, H.X., Fang, C.: Head pose estimation based on random forests for multiclass classification. In: ICPR (2010)
Vezhnevets, V., Sazonov, V., Andreeva, A.: A survey on Pixel-Based skin color detection techniques. In: GraphiCon (2003)
Fanelli, G., Dantone, M., Gall, J., Fossati, A., Van Gool, L.: Random forests for real time 3D face analysis. IJCV 101(3), 437–458 (2013)
Redondo-Cabrera, C., Lopez-Sastre, R., Tuytelaars, T.: All together now: simultaneous object detection and continuous pose estimation using a hough forest with probabilistic locally enhanced voting. In: BMVC (2014)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Gall, J., Yao, A., Razavi, N., van Gool, L., Lempitsky, V.: Hough forests for object detection, tracking, and action recognition. PAMI 33(11), 2188–2202 (2011)
Criminisi, A., Shotton, J., Konukoglu, E.: Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. FTCGV 7(2–3), 81–227 (2012)
Riegler, G., Ruther, M., Bischof, B.: Hough networks for head pose estimation and facial feature localization. In: BMVC (2014)
Ghodrati, A., Pedersoli, M., Tuytelaars, T.: Is 2D information enough for viewpoint estimation? In: BMVC (2014)
Fanelli, G., Weise, T., Gall, J., Van Gool, L.J.: Real time head pose estimation from consumer depth cameras. In: GAPR (2011)
Fanelli, G., Gall, J., Van Gool, L.J.: Real time head pose estimation with random regression forests. In: CVPR (2011)
Murphy-Chutorian, E., Trivedi, M.M.: Head pose estimation in computer vision: a survey. PAMI 31, 607–626 (2009)
Demirkus, M., Precup, D., Clark, J.J., Arbel, T.: Probabilistic temporal head pose estimation using a hierarchical graphical model. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part I. LNCS, vol. 8689, pp. 328–344. Springer, Heidelberg (2014)
Welch, G., Bishop, G.: An Introduction to the Kalman Filter. Technical report (2006)
Isard, M., Blake, A.: CONDENSATION - conditional density propagation for visual tracking. IJCV 29(1), 5–28 (1998)
Acknowledgements
This work is supported by projects CCG2013/EXP-047, CCG2014/EXP-054, TEC2013-45183-R, SPIP2014-1468, ERC Starting Grant COGNIMUND and the MECD Collaboration Grants 2014/15.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
García-Montero, M., Redondo-Cabrera, C., López-Sastre, R., Tuytelaars, T. (2015). Fast Head Pose Estimation for Human-Computer Interaction. In: Paredes, R., Cardoso, J., Pardo, X. (eds) Pattern Recognition and Image Analysis. IbPRIA 2015. Lecture Notes in Computer Science(), vol 9117. Springer, Cham. https://doi.org/10.1007/978-3-319-19390-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-19390-8_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19389-2
Online ISBN: 978-3-319-19390-8
eBook Packages: Computer ScienceComputer Science (R0)