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Performance Analysis of Eye localization Methods for Real Time Vision Interface using Low Grade Video Camera

by Krupa Jariwala, Upena Dalal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 114 - Number 2
Year of Publication: 2015
Authors: Krupa Jariwala, Upena Dalal
10.5120/19952-1777

Krupa Jariwala, Upena Dalal . Performance Analysis of Eye localization Methods for Real Time Vision Interface using Low Grade Video Camera. International Journal of Computer Applications. 114, 2 ( March 2015), 33-40. DOI=10.5120/19952-1777

@article{ 10.5120/19952-1777,
author = { Krupa Jariwala, Upena Dalal },
title = { Performance Analysis of Eye localization Methods for Real Time Vision Interface using Low Grade Video Camera },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 114 },
number = { 2 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 33-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume114/number2/19952-1777/ },
doi = { 10.5120/19952-1777 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:51:39.024070+05:30
%A Krupa Jariwala
%A Upena Dalal
%T Performance Analysis of Eye localization Methods for Real Time Vision Interface using Low Grade Video Camera
%J International Journal of Computer Applications
%@ 0975-8887
%V 114
%N 2
%P 33-40
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

It is necessary to correctly and precisely achieve eye localization, which is a fundamental step for the initialization for other eye localization based applications. There are various methods including special equipment based methods and image based methods to perform this task. Special equipment based methods are very accurate but not practical for day to day use. Image based approaches are user friendly, allows free head movement, avoids specialized hardware and infrared exposure but more difficult to implement. Performance is analysed for state of the art eye localization methods for real time vision interface using low grade camera that use similar objective criterion for error measurement on standard dataset for fair judgment. Finally their localization results are compared based on various error values and rank.

References
  1. K N Jariwala, U D Dalal, Efficient Performance Evaluation for Robust Eye Localization System, International Journal of Computer Science Trends and Technology (IJCST), Volume 3 Issue 1, Jan-Feb 2015.
  2. Hansen, D. and Ji, Q, In the eye of the beholder: A survey of models for eyes and gaze. IEEE Trans. on PAMI, 32(3):478–500, 2010
  3. O. Jesorsky, K. J. Kirchberg, and R. W. Frischholz. Robust face detection using the hausdorff distance. Lecture Notes in Computer Science, LNCS2091 pp 90-95, June 2001.
  4. www. bioid. com/About/BioID-Face-Database
  5. P. Campadelli, R. Lanzarotti, G. Lipori, Eyelocalization : a survey, in: The Fundamentals of Verbal and Non-verbal Communication and the Biometrical Issue NATO Science Series, vol. 18, pp. 234-245, 2007.
  6. Cristinacce, D. , Cootes, T. , and Scott, I. (2004). A multistage approach to facial feature detection. In Proceedings of the 15th BMVC, pages 277–286, England.
  7. Viola, P. and Jones, M, Robust real-time face detection. IJCV, 57(2): 137-154, 2004.
  8. M. Hamouz, J. Kittler, J. K. Kamarainen, P. Paalanen, H. Kälviäinen, and J. Matas. Feature-based affine invariant localization of faces. IEEE Trans. Pattern Analysis and Machine Intelligence, 27(9):1490– 1495, 2005.
  9. Z. Niu, S. Shan, S. Yan, X. Chen, and W. Gao. 2D Cascaded AdaBoost for Eye Localization. Proc. Of the 18th Internationl Conference on Pattern Recognition, 2006.
  10. T¨urkan, M. , Pard`as, M. , and Cetin A. , Human eye localization using edge projections. In Proceedings of the VISAPP, pages 410–415, 2007.
  11. Valenti, R. and Gevers, T. , Accurate eye center location and tracking using isophote curvature. In Proceedings of the CVPR, pages 1–8, 2008.
  12. Asadifard, M. and Shanbezadeh, J. , Automatic adaptive center of pupil detection using face detection and cdf analysis. In Proceedings of the IMECS, volume I, pages 130–133, 2010.
  13. Fei Yang, Junzhou Huang, Peng Yang and Dimitris Metaxas, Eye Localization through Multiscale Sparse Dictionaries, IEEE International Conference on Automatic Face and Gesture Recognition, Santa Barbara, March. 2011.
  14. F. Timm and E. Barth, "Accurate eye centre localisation by means of gradients," in Proc. VISAPP, pp. 125–130, March 2011.
  15. D. Yi, Z. Lei, and S. Z. Li, "A robust eye localization method for low quality face images," in Proceedings of the International Joint Conference on Biometrics (IJCB '11), pp. 1–6, Washington, DC, USA, October 2011.
  16. Yan Ren, Shuang Wang, Biao Hou, Jingjing Ma, A Novel Eye Localization Method With Rotation Invariance, IEEE Transactions On Image Processing, VOL. 23, NO. 1, January 2014.
  17. Fengyi Song, Xiaoyang Tan, Songcan Chen, Zhi-Hua Zhou, A literature survey on robust and efficient eye localization in real-life scenarios, Pattern Recognition Volume 46, Issue 12, Pages 3157–3173 December 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Eye Localization Performance Analysis.

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