Skip to main content
SpringerLink
Log in

Human Eye Detector with Light-Weight and Efficient Convolutional Neural Network

  • Conference paper
  • First Online:
Advances in Computational Collective Intelligence (ICCCI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1287))

Included in the following conference series:

Abstract

The human eye detection plays an important role in computer vision. Along with face detection, it is widely applied in practical security, surveillance, and warning systems such as eye tracking system, eye disease detection, gaze detection, eye blink, and drowsiness detection system. There have been many studies to detect eyes from applying traditional methods to using modern methods based on machine learning and deep learning. This network is deployed with two main blocks, namely the feature extraction block and the detection block. The feature extraction block starts with the use of the convolution layers, C.ReLU (Concatenated Rectified Linear Unit) module, and max pooling layers alternately, followed by the last six inception modules and four convolution layers. The detection block is constructed by two sibling convolution layers using for classification and regression. The experiment was trained and tested on CEW (Closed Eyes In The Wild), BioID Face and GI4E (Gaze Interaction for Everybody) dataset with the results achieved 96.48%, 99.58%, and 75.52% of AP (Average Precision), respectively. The speed was tested in real-time by 37.65 fps (frames per second) on Intel Core I7-4770 CPU @ 3.40 GHz.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • 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

References

  1. Araujo, G., Ribeiro, F., da Silva, E., Goldenstein, S.: Fast eye localization without a face model using inner product detectors. In: 2014 IEEE International Conference on Image Processing, ICIP 2014, pp. 1366–1370 (2015). https://doi.org/10.1109/ICIP.2014.7025273

  2. Chen, S., Liu, C.: Eye detection using discriminatory HAAR features and a new efficient SVM. Image Vision Comput. 33(C), 68–77 (2015). https://doi.org/10.1016/j.imavis.2014.10.007

    Article  Google Scholar 

  3. Chinsatit, W., Saitoh, T.: CNN-based pupil center detection for wearable gaze estimation system. Appl. Comput. Intell. Soft Comput. 2017, 1–10 (2017). https://doi.org/10.1155/2017/8718956

    Article  Google Scholar 

  4. Cortacero, K., Fischer, T., Demiris, Y.: RT-BENE: a dataset and baselines for real-time blink estimation in natural environments. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 1159–1168 (2019)

    Google Scholar 

  5. Deng, J., et al.: The Menpo benchmark for multi-pose 2D and 3D facial landmark localisation and tracking. Int. J. Comput. Vis. 127(6–7), 599–624 (2019). https://doi.org/10.1007/s11263-018-1134-y

    Article  Google Scholar 

  6. Fuhl, W., Santini, T., Kasneci, G., Kasneci, E.: PupilNet: convolutional neural networks for robust pupil detection. CoRR abs/1601.04902 (2016). http://arxiv.org/abs/1601.04902

  7. Leo, M., Cazzato, D., Marco, T., Distante, C.: Unsupervised approach for the accurate localization of the pupils in near-frontal facial images. J. Electr. Imaging 22, 033033 (2013). https://doi.org/10.1117/1.JEI.22.3.033033

    Article  Google Scholar 

  8. Markuš, N., Frljak, M., Pandžić, I., Ahlberg, J., Forchheimer, R.: Eye pupil localization with an ensemble randomized trees. Pattern Recognit. 47, 578–587 (2014). https://doi.org/10.1016/j.patcog.2013.08.008

    Article  Google Scholar 

  9. Mosa, A.H., Ali, M., Kyamakya, K.: A computerized method to diagnose strabismus based on a novel method for pupil segmentation. In: Proceedings of the International Symposium on Theoretical Electrical Engineering (ISTET 2013) (2013)

    Google Scholar 

  10. Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. CoRR abs/1506.01497 (2015). http://arxiv.org/abs/1506.01497

  11. Shang, W., Sohn, K., Almeida, D., Lee, H.: Understanding and improving convolutional neural networks via concatenated rectified linear units. CoRR abs/1603.05201 (2016). http://arxiv.org/abs/1603.05201

  12. Sharma, R., Savakis, A.: Lean histogram of oriented gradients features for effective eye detection. J. Electr. Imaging 24, 063007 (2015). https://doi.org/10.1117/1.JEI.24.6.063007

    Article  Google Scholar 

  13. Swirski, L., Bulling, A., Dodgson, N.: Robust real-time pupil tracking in highly off-axis images. In: Eye Tracking Research and Applications Symposium (ETRA), pp. 173–176 (2012). https://doi.org/10.1145/2168556.2168585

  14. Szegedy, C., et al.: Going deeper with convolutions. CoRR abs/1409.4842 (2014). http://arxiv.org/abs/1409.4842

  15. Timm, F., Barth, E.: Accurate eye centre localisation by means of gradients. In: VISAPP (2011)

    Google Scholar 

  16. Valenti, R., Gevers, T.: Accurate eye center location through invariant isocentric patterns. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1785–98 (2012). https://doi.org/10.1109/TPAMI.2011.251

    Article  Google Scholar 

  17. Wu, Y., Ji, Q.: Constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3400–3408 (2016)

    Google Scholar 

  18. Wu, Y., Ji, Q.: Facial landmark detection: a literature survey. CoRR abs/1805.05563 (2018). http://arxiv.org/abs/1805.05563

  19. Xiao, S., Feng, J., Xing, J., Lai, H., Yan, S., Kassim, A.: Robust facial landmark detection via recurrent attentive-refinement networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 57–72. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_4

    Chapter  Google Scholar 

  20. Zadeh, A., Chong Lim, Y., Baltrusaitis, T., Morency, L.P.: Convolutional experts constrained local model for 3D facial landmark detection. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 2519–2528 (2017)

    Google Scholar 

  21. Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., Li, S.Z.: Faceboxes: a CPU real-time face detector with high accuracy. CoRR abs/1708.05234 (2017). http://arxiv.org/abs/1708.05234

Download references

Acknowledgement

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government. (MSIT) (2020R1A2C2008972)

Author information

Authors and Affiliations

  1. School of Electrical Engineering, University of Ulsan, Ulsan, Korea

    Duy-Linh Nguyen, Muhamad Dwisnanto Putro & Kang-Hyun Jo

Authors
  1. Duy-Linh Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhamad Dwisnanto Putro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kang-Hyun Jo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Duy-Linh Nguyen , Muhamad Dwisnanto Putro or Kang-Hyun Jo .

Editor information

Editors and Affiliations

  1. Wroclaw University of Economics and Business, Wrocław, Poland

    Marcin Hernes

  2. Wrocław University of Science and Technology, Wrocław, Poland

    Krystian Wojtkiewicz

  3. University of Newcastle, Newcastle, Australia

    Edward Szczerbicki

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nguyen, DL., Putro, M.D., Jo, KH. (2020). Human Eye Detector with Light-Weight and Efficient Convolutional Neural Network. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. https://doi.org/10.1007/978-3-030-63119-2_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-63119-2_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63118-5

  • Online ISBN: 978-3-030-63119-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation