[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Outlier Constrained Unsupervised Domain Adaptation Algorithm for Gaze Estimation

  • Conference paper
  • First Online:
Intelligent Robotics and Applications (ICIRA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13455))

Included in the following conference series:

  • 2878 Accesses

Abstract

In recent years, gaze estimation has been applied to numerous application areas, such as driver monitor system, autism assessment, and so on. However, current practical gaze estimation algorithms require a large amount of data to obtain better results. The collection of gaze data requires specific equipment, and the collection process is cumbersome, tedious and lengthy. Moreover, in some scenarios, like the autism assessment scenario, it is impossible to obtain the gaze training data of autistic children due to their social communication disorders. Therefore, we need to generalize a model trained on public datasets to a new scenario without gaze ground truth labels. In this study, we tackle this problem by leveraging adversarial learning to implement domain adaptation. Besides, we propose an outlier loss to supervise the outputs of the target domain. We test our domain adaptation algorithm on the XGaze-to-MPII domain adaptation task, and achieve a performance improvement of 14.7%.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 87.50
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 109.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Cui, S., Wang, S., Zhuo, J., Su, C., Huang, Q., Tian, Q.: Gradually vanishing bridge for adversarial domain adaptation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12455–12464 (2020)

    Google Scholar 

  2. Funes Mora, K.A., Monay, F., Odobez, J.M.: Eyediap: a database for the development and evaluation of gaze estimation algorithms from RGB and RGB-D cameras. In: Proceedings of the Symposium on Eye Tracking Research and Applications, pp. 255–258 (2014)

    Google Scholar 

  3. Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International Conference on Machine Learning, pp. 1180–1189. PMLR (2015)

    Google Scholar 

  4. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)

    Google Scholar 

  5. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  6. He, Z., Spurr, A., Zhang, X., Hilliges, O.: Photo-realistic monocular gaze redirection using generative adversarial networks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6932–6941 (2019)

    Google Scholar 

  7. Lahiri, A., Agarwalla, A., Biswas, P.K.: Unsupervised domain adaptation for learning eye gaze from a million synthetic images: an adversarial approach. In: Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing, pp. 1–9 (2018)

    Google Scholar 

  8. Lee, D.H., et al.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on Challenges in Representation Learning, ICML, vol. 3, pp. 896 (2013)

    Google Scholar 

  9. Lian, D., et al.: RGBD based gaze estimation via multi-task CNN. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 2488–2495 (2019)

    Google Scholar 

  10. Liu, Y., Liu, R., Wang, H., Lu, F.: Generalizing gaze estimation with outlier-guided collaborative adaptation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3835–3844 (2021)

    Google Scholar 

  11. Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: International Conference on Machine Learning, pp. 97–105. PMLR (2015)

    Google Scholar 

  12. Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: Advances in Neural Information Processing systems, vol. 31 (2018)

    Google Scholar 

  13. Sun, B., Feng, J., Saenko, K.: Return of frustratingly easy domain adaptation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30 (2016)

    Google Scholar 

  14. Sun, L., Liu, Z., Sun, M.T.: Real time gaze estimation with a consumer depth camera. Inf. Sci. 320, 346–360 (2015)

    Article  MathSciNet  Google Scholar 

  15. Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  16. Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7167–7176 (2017)

    Google Scholar 

  17. Vicente, F., Huang, Z., Xiong, X., De la Torre, F., Zhang, W., Levi, D.: Driver gaze tracking and eyes off the road detection system. IEEE Trans. Intell. Transp. Syst. 16(4), 2014–2027 (2015)

    Article  Google Scholar 

  18. Villanueva, A., Cabeza, R.: A novel gaze estimation system with one calibration point. IEEE Trans. Syst. Man Cybern. Part B Cybern. 38(4), 1123–1138 (2008)

    Article  Google Scholar 

  19. Wang, B., Hu, T., Li, B., Chen, X., Zhang, Z.: Gatector: a unified framework for gaze object prediction. arXiv preprint arXiv:2112.03549 (2021)

  20. Wang, K., Zhao, R., Ji, Q.: Human computer interaction with head pose, eye gaze and body gestures. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 789–789. IEEE (2018)

    Google Scholar 

  21. Wang, K., Zhao, R., Su, H., Ji, Q.: Generalizing eye tracking with Bayesian adversarial learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11907–11916 (2019)

    Google Scholar 

  22. Wang, Z., Liu, J., He, K., Xu, Q., Xu, X., Liu, H.: Screening early children with autism spectrum disorder via response-to-name protocol. IEEE Trans. Industr. Inf. 17(1), 587–595 (2019)

    Article  Google Scholar 

  23. Yu, Y., Odobez, J.M.: Unsupervised representation learning for gaze estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7314–7324 (2020)

    Google Scholar 

  24. Zhang, X., Park, S., Beeler, T., Bradley, D., Tang, S., Hilliges, O.: ETH-XGaze: a large scale dataset for gaze estimation under extreme head pose and gaze variation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12350, pp. 365–381. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58558-7_22

    Chapter  Google Scholar 

  25. Zhang, X., Sugano, Y., Fritz, M., Bulling, A.: Appearance-based gaze estimation in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4511–4520 (2015)

    Google Scholar 

  26. Zhang, X., Sugano, Y., Fritz, M., Bulling, A.: It’s written all over your face: full-face appearance-based gaze estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 51–60 (2017)

    Google Scholar 

  27. Zhou, X., Cai, H., Li, Y., Liu, H.: Two-eye model-based gaze estimation from a Kinect sensor. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 1646–1653. IEEE (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Honghai Liu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, H., Wang, X., Ren, W., Lin, R., Liu, H. (2022). Outlier Constrained Unsupervised Domain Adaptation Algorithm for Gaze Estimation. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13455. Springer, Cham. https://doi.org/10.1007/978-3-031-13844-7_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-13844-7_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13843-0

  • Online ISBN: 978-3-031-13844-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics