Abstract
Eyelid identification provides key data that can be used in several application such as controlling gaze-based HMIs (human machine interfaces), the design of new diagnostic tools for brain diseases, improving driver safety, drowsiness detection, research on advertisement, etc. We propose a novel eyetracking algorithm by learning a deep deconvolutional neural network. To train and test our method, we use several data sets with hand-labeled eye images from real-world tasks. Our method outperforms previous eye tracking methods, improving the results of the current state of the art in a 19%.
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
Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)
Fuhl, W., Kübler, T., Sippel, K., Rosenstiel, W., Kasneci, E.: ExCuSe: robust pupil detection in real-world scenarios. In: Azzopardi, G., Petkov, N. (eds.) CAIP 2015. LNCS, vol. 9256, pp. 39–51. Springer, Cham (2015). doi:10.1007/978-3-319-23192-1_4
Fuhl, W., Santini, T., Kasneci, G., Kasneci, E.: Pupilnet: convolutional neural networks for robust pupil detection. arXiv preprint arXiv:1601.04902 (2016)
Fuhl, W., Santini, T.C., Kübler, T., Kasneci, E.: Else: ellipse selection for robust pupil detection in real-world environments. In: Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research and Applications, pp. 123–130. ACM (2016)
Kasneci, E., Sippel, K., Aehling, K., Heister, M., Rosenstiel, W., Schiefer, U., Papageorgiou, E.: Driving with binocular visual field loss? a study on a supervised on-road parcours with simultaneous eye and head tracking. PLoS One 9(2), e87470 (2014)
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)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015
McKinley, R., Gundersen, T., Wagner, F., Chan, A., Wiest, R., Reyes, M.: Nabla-net: a deep dag-like convolutional architecture for biomedical image segmentation: application to white-matter lesion segmentation in multiple sclerosis. In: MSSEG Challenge Proceedings: Multiple Sclerosis Lesions Segmentation Challenge Using a Data Management and Processing Infrastructure, p. 37 (2016)
Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1520–1528 (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. ArXiv e-prints, May 2015
Acknowledgement
This work was partially funded by project DPI2015-68664-C4-1-R of the Spanish Ministry of Economy and by Banco de Santander and Universidad Rey Juan Carlos Funding Program for Excellence Research Groups ref. Computer Vision and Image Processing (CVIP). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Nvidia Tesla K40 GPU used for this research.
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
Vera-Olmos, F.J., Malpica, N. (2017). Deconvolutional Neural Network for Pupil Detection in Real-World Environments. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Biomedical Applications Based on Natural and Artificial Computing. IWINAC 2017. Lecture Notes in Computer Science(), vol 10338. Springer, Cham. https://doi.org/10.1007/978-3-319-59773-7_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-59773-7_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59772-0
Online ISBN: 978-3-319-59773-7
eBook Packages: Computer ScienceComputer Science (R0)