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Chinese Conference on Biometric Recognition

CCBR 2017: Biometric Recognition pp 562–571Cite as

The Android-Based Acquisition and CNN-Based Analysis for Gaze Estimation in Eye Tracking

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10568))

Abstract

Over the past several years, the demand for eye tracking is increasing across fields of computer vision and pattern recognition, especially in commercial applications. However, the low prediction accuracy and the restriction of datasets and methods for special eye tracking equipment have been obstacles of the wide application of gaze estimation. In this paper, we develop an Android-based acquisition software named EyeTracker, to collect the first Chinese gaze dataset. And then we proposed a convolutional neural network framework for gaze estimation in eye tracking based on a single image. We evaluate our proposed analysis model on our dataset-EyeTrackD (tablet) and Gazecapture (part of phone data). Our model achieves a prediction error of 4.33 cm and 2.25 cm on these two datasets respectively, which are better than the previous method using the same data. Extensive experiments under different network settings show the effectiveness of our convolutional neural network framework.

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References

  1. Huey, E.B.: The psychology and pedagogy of reading. J. Philos. 18(5), 500–502 (1908)

    Google Scholar 

  2. Hansen, D.W., Ji, Q.: In the eye of the beholder: a survey of models for eyes and gaze. IEEE Trans. Pattern Anal. Mach. Intell. 32(3), 478–500 (2010)

    Article  Google Scholar 

  3. Hennessey, C., Noureddin, B., Lawrence, P.: A single camera eye-gaze tracking system with free head motion. In: Proceedings of ETRA, pp. 87–94 (2006)

    Google Scholar 

  4. Yoo, D.H., Chung, M.J.: A novel non-intrusive eye gaze estimation using cross-ratio under large head motion. Comput. Vis. Image Underst. 98(1), 25–51 (2005)

    Article  Google Scholar 

  5. Zhu, Z., Ji, Q.: Eye gaze tracking under natural head movements. In: Proceedings of CVPR, pp. 918–923 (2005)

    Google Scholar 

  6. Zhu, Z., Ji, Q., Bennett, K.P.: Nonlinear eye gaze mapping function estimation via support vector regression. In: Proceedings of ICPR, pp. 1132–1135 (2006)

    Google Scholar 

  7. Chen, J., Ji, Q.: 3D gaze estimation with a single camera without IR illumination. In: Proceedings of ICPR, pp. 1–4 (2008)

    Google Scholar 

  8. Yamazoe, H., Utsumi, A., Yonezawa, T., Abe, S.: Remote gaze estimation with a single camera based on facial-feature tracking without special calibration actions. In: Proceedings of ETRA, pp. 245–250 (2008)

    Google Scholar 

  9. 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)

    Article  MATH  MathSciNet  Google Scholar 

  10. Krafka, K., Khosla, A., Kellnhofer, P., Kannan, H., Bhandarkar, S., Matusik, W.: Eye tracking for everyone. In: Proceedings of CVPR, pp. 2176–2184 (2016)

    Google Scholar 

  11. Huang, Q., Veeraraghavan, A., Sabharwal, A.: TabletGaze: unconstrained appearance-based gaze estimation in mobile tablets. Comput. Sci. (2015)

    Google Scholar 

  12. Chen, J., Ji, Q.: Probabilistic gaze estimation without active personal calibration. In: CVPR 2011, pp. 609–616 (2011)

    Google Scholar 

  13. Sugano, Y., Matsushita, Y., Sato, Y.: Appearance-based gaze estimation using visual saliency. PAMI 35(2), 329–341 (2013)

    Article  Google Scholar 

  14. Sugano, Y., Matsushita, Y., Sato, Y., Koike, H.: An incremental learning method for unconstrained gaze estimation. Comput. Sci. 5304(3), 656–667 (2008)

    Google Scholar 

  15. Mora, K.A.F., Monay, F., Odobez, J.M.: EYEDIAP: a database for the development and evaluation of gaze estimation algorithms from rgb and rgb-d cameras. In: ACM Symposium on Eye Tracking Research and Applications, pp. 255–258 (2014)

    Google Scholar 

  16. Sugano, Y., Matsushita, Y., Sato, Y.: Learning-by-synthesis for appearance-based 3D gaze estimation. In: CVPR 2014, pp. 1821–1828 (2014)

    Google Scholar 

  17. Zhang, X., Sugano, Y., Fritz, M., Bulling, A.: Appearance-based gaze estimation in the wild. In: CVPR 2015, pp. 4511–4520 (2015)

    Google Scholar 

  18. Szegedy, C., Ioffe, S., et al.: Inception-v4, Inception-ResNet and the impact of residual connections on learning. In: AAAI 2016, pp. 4278–4284 (2016)

    Google Scholar 

  19. He, K., Zhang, X., Ren, S., Sun, J.: Identity Mappings in Deep Residual Networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). doi:10.1007/978-3-319-46493-0_38

    Chapter  Google Scholar 

  20. Ioffe, S., Sergey C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. Comput. Sci. (2015)

    Google Scholar 

  21. Jiwon, K., Jung, K.L., Kyoung, M.L.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of CVPR 2016, pp. 1646–1654 (2016)

    Google Scholar 

  22. Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: IEEE Conference on CVPR, pp. 1891–1898 (2014)

    Google Scholar 

  23. Zhu, X., Ramanan, D.: Face detection, pose estimation,and landmark localization in the wild. In: CVPR 2012, pp. 2879–2886 (2012)

    Google Scholar 

  24. Russakovsky, O., Deng, J., Su, J.: ImageNet Large Scale Visual Recognition Challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)

    Article  MathSciNet  Google Scholar 

  25. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems Curran Associates Inc., pp. 1097–1105 (2012)

    Google Scholar 

  26. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640–651 (2017)

    Article  Google Scholar 

  27. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICRL 2015, pp. 1–14 (2015)

    Google Scholar 

  28. Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3476–3483. IEEE Computer Society (2013)

    Google Scholar 

  29. Zhou, E., Fan, H., Cao, Z.: Extensive facial landmark localization with coarse-to-fine convolutional network cascade. In: IEEE International Conference on Computer Vision Workshops, pp. 386–391. IEEE (2013)

    Google Scholar 

  30. Zhang, K., Zhang, Z., Li, Z.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Sig. Process. Lett. 23(10), 1499–1503 (2016)

    Article  Google Scholar 

  31. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. arXiv:1408.5093 (2014)

  32. Kingma, D., Diederik, P., Ba, J.: Adam: a method for stochastic optimization. Comput. Sci. (2014)

    Google Scholar 

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Acknowledgements

This work is partially supported by the National Natural Science Foundation for Young Scientists of China (no. 61402289), and National Science Foundation of Guangdong Province (no. 2014A030313558).

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Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518000, China

    Wei Wen, Tong Chen & Meng Yang

  2. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, 510000, China

    Meng Yang

Authors
  1. Wei Wen
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  2. Tong Chen
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  3. Meng Yang
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Corresponding author

Correspondence to Meng Yang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Jie Zhou

  2. Beihang University, Beijing, China

    Yunhong Wang

  3. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  4. Computing and Technology, Chinese Academy of Sciences, Beijing, China

    Yong Xu

  5. Shenzhen University, Shenzhen, China

    Linlin Shen

  6. Tsinghua University, Beijing, China

    Jianjiang Feng

  7. Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

  8. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Yu Qiao

  9. Graduate School at Shenzhen, Tsinghua University, Shenzhen, China

    Zhenhua Guo

  10. Shenzhen University, Shenzhen, China

    Shiqi Yu

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Wen, W., Chen, T., Yang, M. (2017). The Android-Based Acquisition and CNN-Based Analysis for Gaze Estimation in Eye Tracking. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_61

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  • DOI: https://doi.org/10.1007/978-3-319-69923-3_61

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69922-6

  • Online ISBN: 978-3-319-69923-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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