Abstract
As an inherent attribute of human, head pose plays an important role in many tasks. In this paper, we formulate head pose estimation in different directions as a multi-task regression problem, and propose a fast, compact and robust head pose estimation model, named TinyPoseNet. Specifically, we combine the tasks of head pose estimation in different directions into one joint learning task and design the whole model based on the principle of “being deeper” and “being thinner” to obtain a tiny model with specially designed types and particular small numbers of filters. We perform thorough experiments on 3 types of test sets and compare our method with others from several different aspects, including the accuracy, the speed, the compactness and so on. In addition, we introduce large angle data in Multi-PIE to verify the ability of dealing with large-scale pose in practice. All the experiments demonstrate the advantages of the proposed model.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). doi:10.1007/978-3-319-10590-1_53
Kuchinsky, A., Pering, C., Creech, M.L., Freeze, D., Serra, B., Gwizdka, J.: Fotofile: a consumer multimedia organization and retrieval system. In: Proceedings of the SIGCHI conference on Human Factors in Computing Systems, pp. 496–503. ACM (1999)
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) 2015, pp. 4624–4628. IEEE (2015)
Wang, C., Song, X.: Robust head pose estimation via supervised manifold learning. Neural Networks 53, 15–25 (2014)
Krüger, N., Pötzsch, M., von der Malsburg, C.: Determination of face position and pose with a learned representation based on labelled graphs. Image Vis. Comput. 15(8), 665–673 (1997)
Wu, J., Trivedi, M.M.: A two-stage head pose estimation framework and evaluation. Pattern Recogn. 41(3), 1138–1158 (2008)
Ahn, B., Park, J., Kweon, I.S.: Real-time head orientation from a monocular camera using deep neural network. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9005, pp. 82–96. Springer, Cham (2015). doi:10.1007/978-3-319-16811-1_6
Yan, Y., Ricci, E., Subramanian, R., Liu, G., Lanz, O., Sebe, N.: A multi-task learning framework for head pose estimation under target motion. IEEE Trans. Pattern Anal. Mach. Intell. 38(6), 1070–1083 (2016)
Mukherjee, S.S., Robertson, N.M.: Deep head pose: gaze-direction estimation in multimodal video. IEEE Trans. Multimedia 17(11), 2094–2107 (2015)
Liu, X., Kan, M., Wu, W., Shan, S., Chen, X.: Viplfacenet: an open source deep face recognition sdk. arXiv preprint arXiv:1609.03892 (2016)
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)
Han, S., Mao, H., Dally, W.J.: Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015)
Lin, M., Chen, Q., Yan, S.: Network in network. Computer Science (2014)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-pie. Image Vis. Comput. 28(5), 807–813 (2010)
Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhang, X., Zhao, D.: The cas-peal large-scale chinese face database and baseline evaluations. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 38(1), 149–161 (2008)
Bansal, A., Nanduri, A., Castillo, C., Ranjan, R., Chellappa, R.: Umdfaces: An annotated face dataset for training deep networks. arXiv preprint arXiv:1611.01484 (2016)
Davis, L.S., Gonzalez, J., Haj, M.A.: On partial least squares in head pose estimation: How to simultaneously deal with misalignment. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2602–2609 (2012)
Heo, J., Savvides, M.: Face recognition across pose using view based active appearance models (vbaams) on cmu multi-pie dataset. In: International Conference on Computer Vision Systems, pp. 527–535 (2008)
Zhu, X., Lei, Z., Yan, J., Yi, D., Li, S.Z.: High-fidelity pose and expression normalization for face recognition in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 787–796 (2015)
Yi, D., Lei, Z., Liao, S., Li, S.: Learning face representation from scratch. arXiv preprint arXiv:1411.7923 (2014)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. arXiv preprint arXiv:1409.4842 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Li, S., Wang, L., Yang, S., Wang, Y., Wang, C. (2017). TinyPoseNet: A Fast and Compact Deep Network for Robust Head Pose Estimation. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-70096-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70095-3
Online ISBN: 978-3-319-70096-0
eBook Packages: Computer ScienceComputer Science (R0)