Abstract
In this paper, we propose a new iris center localization algorithm based on Snakuscule and 3D eyeball model. Firstly, the initial iris center is obtained from the facial feature key-points by face alignment algorithm. Then we reduce the error caused by the low-quality images by judging the state of eye region. We establish a 3D eyeball model, which reflects the geometric relationship among iris center, eye center and iris contour. To further obtain the accurate iris center location, we put forward an improved Snakuscule energy model. The energy value is obtained by initializing a fixed size of Snakuscule model and combining with the 3D eyeball model. The iris contour is updated iteratively according to the energy value, and the final iris center is obtained. Finally, experiments conducted on BioID face dataset validate the effectiveness and superiority of our method. The maximum standard error of the algorithm reached 85.0%, 97.8% and 99.8% respectively for \(e\le 0.05\), \(e\le 0.1\), and \(e\le 0.25\).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Asteriadis, S., Nikolaidis, N., Hajdu, A., Pitas, I.: An eye detection algorithm using pixel to edge information. In: International Symposium on Communications, Control and Signal Processing (2006)
Baek, S.J., Choi, K.A., Ma, C., Kim, Y.H., Ko, S.J.: Eyeball model-based iris center localization for visible image-based eye-gaze tracking systems. IEEE Trans. Consum. Electron. 59(2), 415–421 (2013)
Cai, H., Liu, B., Zhang, J., Chen, S., Liu, H.: Visual focus of attention estimation using eye center localization. IEEE Syst. J. 11(3), 1320–1325 (2015)
Campadelli, P., Lanzarotti, R., Lipori, G.: Precise eye localization through a general-to-specific model definition. In: BMVC, vol. 1, pp. 187–196 (2006)
Garg, S., Tripathi, A., Cutrell, E.: Accurate eye center localization using snakuscule. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–8. IEEE (2016)
Kim, S., Chung, S.T., Jung, S., Oh, D., Kim, J., Cho, S.: Multi-scale gabor feature based eye localization. World Acad. Sci. Eng. Technol. 21, 483–487 (2007)
Koenderink, J.J., Van Doorn, A.J.: Surface shape and curvature scales. Image Vis. Comput. 10(8), 557–564 (1992)
Kroon, B., Hanjalic, A., Maas, S.M.: Eye localization for face matching: is it always useful and under what conditions? In: Proceedings of the 2008 International Conference on Content-based Image and Video Retrieval, pp. 379–388 (2008)
Laddi, A., Prakash, N.R.: An augmented image gradients based supervised regression technique for iris center localization. Multimed. Tools Appl. 76(5), 7129–7139 (2016). https://doi.org/10.1007/s11042-016-3361-y
Leo, M., Cazzato, D., De Marco, T., Distante, C.: Unsupervised eye pupil localization through differential geometry and local self-similarity matching. PLoS ONE 9(8), e102829 (2014)
Niu, Z., Shan, S., Yan, S., Chen, X., Gao, W.: 2D cascaded adaboost for eye localization. In: 18th International Conference on Pattern Recognition (ICPR 2006), vol. 2, pp. 1216–1219. IEEE (2006)
Timm, F., Barth, E.: Accurate eye centre localisation by means of gradients. Visapp 11, 125–130 (2011)
Zhou, X., Jiaqi Jiang, J.L., Chen, S.: An algorithm for iris center location based on three-dimensional eyeball model and snakuscule. Comput. Sci. 46(9), 284–290 (2019)
**ong, X., De la Torre, F.: Supervised descent method and its applications to face alignment. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 532–539 (2013)
Zhang, W., Smith, M.L., Smith, L.N., Farooq, A.: Eye center localization and gaze gesture recognition for human-computer interaction. JOSA A 33(3), 314–325 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tao, J., Wang, C., Li, B. (2022). An Iris Center Localization Method Based on 3D Eyeball Model and Snakuscule. In: Zhai, G., Zhou, J., Yang, H., An, P., Yang, X. (eds) Digital TV and Wireless Multimedia Communications. IFTC 2021. Communications in Computer and Information Science, vol 1560. Springer, Singapore. https://doi.org/10.1007/978-981-19-2266-4_28
Download citation
DOI: https://doi.org/10.1007/978-981-19-2266-4_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-2265-7
Online ISBN: 978-981-19-2266-4
eBook Packages: Computer ScienceComputer Science (R0)