Abstract
Estimating the head pose is an important capability of a robot when interacting with humans. But there are many difficulties of human head pose estimation, such as extreme pose, lighting, and occlusion, has historically hampered. This paper addresses the problem of head pose estimation with two degrees of freedom (pitch and yaw) using a low-resolution image. We propose a method that uses convolutional neural networks for training and classifying various head poses over a wide range of angles from a single image. Evaluations on public head pose database, Prima database, demonstrate that our method achieves better results than 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
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Murphy-Chutorian, E., Trivedi, M.M.: Head pose estimation in computer vision: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 31, 607–626 (2009)
Gourier, N., Hall, D., Crowley, J.L.: Estimating face orientation from robust detection of salient facial structures. In: FG Net Workshop on Visual Observation of Deictic Gestures (2004)
Wu, J., Pedersen, J.M., Putthividhya, D., Norgaard, D., Trivedi, M.M.: A two-level pose estimation framework using majority voting of gabor wavelets and bunch graph analysis. In: ICPR Workshop Visual Observation of Deictic Gestures, Citeseer (2004)
Gourier, N., Maisonnasse, J., Hall, D.: Head pose estimation on low resolution images. In: First International Evaluation Workshop on Classification of Events, Activities and Relationships, pp. 270–280 (2006)
Drouard, V., Ba, S., Evangelidis, G., Deleforge, A., Horaud, R.: Head pose estimation via probabilistic high-dimensional regression. In: IEEE International Conference on Image Processing (ICIP), pp. 4624–4628 (2015)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. IEEE 86, 2278–2324 (1998)
Malagavi, N., Hemadri, V., Kulkarni, U.P.: Head pose estimation using convolutional neural networks. J. Innov. Sci. Eng. Technol. 1, 470–475 (2014)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lee, S., Saitoh, T. (2018). Head Pose Estimation Using Convolutional Neural Network. In: Kim, K., Kim, H., Baek, N. (eds) IT Convergence and Security 2017. Lecture Notes in Electrical Engineering, vol 449. Springer, Singapore. https://doi.org/10.1007/978-981-10-6451-7_20
Download citation
DOI: https://doi.org/10.1007/978-981-10-6451-7_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6450-0
Online ISBN: 978-981-10-6451-7
eBook Packages: EngineeringEngineering (R0)