Abstract
The purpose of this work is to accomplish a study aiming the construction of an eye tracking and iris detection system, based on images obtained from a low-cost webcam. The main objective of the paper is to conduct a comparison between three computer vision approaches for iris detection, trying to identify the more suitable method for application in the aforementioned low-cost eye tracking system. The methods which have achieved the best detection rates were the Projection and Thresholding, however, all of them offer possibilities for application in real-time processing and improvement.
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
References
C. Morimoto, M. Mimica, Eye gaze tracking techniques for interactive applications. Comput. Vis. Image Underst. 98, 4–24 (2005)
D. Torricelli, S. Conforto, M. Schmid, T. DAlessio, A neural-based remote eye gaze tracker under natural head motion. Comput. Methods Programs Biomed. 92(1), 66–78 (2008)
M. Kumar, Reducing the Cost of Eye Tracking Systems. Technical Report CSTR 2006–08, Stanford University, Stanford (2006)
N. Kehtarnavaz, M.N. Gamadia, Real-Time Image and Video Processing: From Research to Reality (Morgan & Claypool, US, 2006)
G. Bradski, A. Kaehler, Learn. OPenCV (OReilly, Sebastopol, 2008)
M. Dobes, J. Martinek, D. Skoupil, Z. Dobesova, J. Pospisil, Human eye localization using the modified Hough transform. Optik 117, 468–473 (2006)
V. Fernandes Junior, M. Marengoni, Detecção e rastreamento de olhos para implementação de uma interface humano-computador, Anais V Workshop de Visão Computacional, Universidade Presbiteriana Mackenzie, São Paulo (2009)
W. Dong, Z. Sun, T. Tan, Z. Wei, Quality-based dynamic threshold for iris matching, in ICIP 2009, IEEE, pp. 1949–1952 (2009)
S. Asteriadis, D. Soufleros, K. Karpouzis, S. Kollias, A Natural Head Pose and Eye Gaze Dataset, in ICMI, Boston, 2–6 November, 2009
G. Crisafulli, G. Iannizzotto, F. La Rosa. Two competitive solutions to the problem of remote eye-tracking. 2nd Conference on Human System Interactions, 2009. pp. 356–362, IEEE (2009)
G. Daunys et al., Report on New Approaches to Eye Tracking. COGAIN, IST-2003-511598: Deliverable 5.2
A.S. Georghiades, P.N. Belhumeur, D.J. Kriegman, From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intelligence 23(6), 643–660 (2001)
M. Haseyama, C. Kaneko, A robust human-eye tracking method in video sequences. ICIP 2005, 362–365 (2005)
M. Nixon, A. Aguado, Feature Extraction & Image Processing, 2nd edn. (Academic Press, New York, 2008)
Y. Chen, M. Adjouadi, C. Han, J. Wang, A. Barreto, N. Rishe, J. Andrian, A highly accurate and computationally efficient approach for unconstrained iris segmentation. Image Vis. Comput. 28(2), 261–269 (2010)
W. Burger, M. Burge, Digital Image Processing: An Algorithmic Introduction Using Java (Springer, New York, 2008)
K. Peng, L. Chen, S. Ruan, G. Kukharev, A robust algorithm for eye detection on gray intensity face without spectacles. J. Comput Sci. Technol. 5(3), 127–132 (2005)
M. Bianchini, L. Sarti, An eye detection system based on neural autoassociators. Artif. Neural Networks Pattern Recognit. 4087, 244–252 (2006)
A.Z. Arifin, A. Asano, Image segmentation by histogram thresholding using hierarchical cluster analysis. Pattern. Recognit. Lett. 27, 1515–1521 (2006)
G. Proença, S. Filipe, R. Santos, J. Oliveira, L.A. Alexandre, The UBIRIS.v2: a database of visible wavelength images captured on-the-move and at-a-distance. IEEE Trans. PAMI. 32(8), 1529–1535 (2010)
B. Martinkauppi, X. Soriano, S. Huovinen , M. Laaksonen, Face video database. CGIV’2002, Poitiers, France, pp. 380–383 (2002)
G. Pan, L. Sun, Z. Wu, S Lao, Eyeblink-based Anti-spoofing in Face Recognition from a Generic Webcamera. ICCV’07, IEEE: Rio de Janeiro, Brazil, October 14–20, (2007)
Acknowledgments
This work was financially supported by FINEP (Financiadora de Estudos e Projetos), FAPESC (Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina), UNOESC (Universidade do Oeste de Santa Catarina), and the CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Kuehlkamp, A., Franco, C.R., Comunello, E. (2014). An Evaluation of Iris Detection Methods for Real-Time Video Processing with Low-Cost Equipment. In: Czachórski, T., Gelenbe, E., Lent, R. (eds) Information Sciences and Systems 2014. Springer, Cham. https://doi.org/10.1007/978-3-319-09465-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-09465-6_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09464-9
Online ISBN: 978-3-319-09465-6
eBook Packages: Computer ScienceComputer Science (R0)