Abstract.
The iris has been proposed as a highly reliable and stable biometric identifier for person authentication/recognition about two decades ago. Since then, most work in the field has been focused on segmentation and matching algorithms able to work on pictures of whole face or eye region typically captured at close distance, while preserving recognition accuracy. In this paper we present an iris matching algorithm based on spatial histograms that, while showing good recognition performance on some of the most referenced public iris dataset, is also able to perform a one-to-one comparison in a small amount of time thanks to its low computing load, thus resulting particularly suited to iris recognition applications on mobile devices.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Daugman, J.: High Confidence Visual Recognition of Persons by a Test of Statistical Independence. IEEE PAMI 15(11), 1148–1161 (1993)
Wildes R.: Iris recognition: an emerging biometric technology. Proceedings of the IEEE 85(9) (1997)
Lim, S., Lee, K., Byeon, O., Kim, T.: Efficient Iris Recognition through Improvement of Feature O. Vector and Classifier. ETRI J. 23(2), 61–70 (2001)
Boles, W.W., Boashash, B.: A Human Identification Technique Using Images of the Iris and Wavelet Transform. IEEE Transactions On Signal Processing 46(4), 1185–1188 (1998)
Proenca, H., Alexandre, L.A.: The NICE.I: Noisy Iris Challenge Evaluation – Part I. In: Proceedings of the IEEE First International Conference on Biometrics: Theory, Applications and Systems (2007)
Bowyer, P., Kevin, W.: The results of the NICE.II Iris biometrics competition. Pattern Recognition Letters 33(8), 965–969 (2011)
Jeong, D.S., Hwang, J.B., Kang, K., Won, C., Park, D., Kim, J.: A new iris segmentation method for non-ideal iris images. Image Vision Computing 28(2), 254–260 (2010)
Shin, K., Nam, G., Jeong, D., Cho, D., Kang, B., Park, K., Kim J.: New iris recognition method for noisy iris images. Pattern Recognition Lett., Special Issue Recognition of Visible Wavelength Iris Images Acquired On-The-Move and At-A-Distance
Jeong, D.S., Park, H.-A., Park, K.R., Kim, J.H.: Iris Recognition in Mobile Phone Based on Adaptive Gabor Filter. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 457–463. Springer, Heidelberg (2005)
Park, K.R., Park, H., Kang, B.Y., Lee, E.C., Jeong, D.S.: A study on iris localization and recognition on mobile phone. Eur. J. Adv. Signal Process, 1–12 (2007)
Kang, J.S.: Mobile iris recognition systems: An emerging biometric technology. In: International Conference on Computational Science (ICCS) (2010)
Cho, D.H., Park, K.R., Rhee, D.W.: Real-Time Iris Localization for Iris Recognition in Cellular Phone. In: Int’,l Conf. Software Eng., Artificial Intelligence, Networking and Parallel/Distributed Computing, pp. 254–259 (2005)
Cho, D.H., Park, K.R., Rhee, D.W., Kim, Y.G., Yang, J.H.: Pupil and iris localization for iris recognition in mobile phones. In: Proc. SNPD, pp.197–201 (2006)
De Marsico, M., Galdi, C., Nappi, M., Riccio, D.: FIRME: Face and Iris Recognition for Mobile Engagement. Image and Vision Computing (2014)
De Marsico, M., Nappi, M., Riccio, D.: ISIS: Iris Segmentation for Identification System. In: ICPR 2010, pp. 2857–2860 (2010)
Taubin, G.: Estimation Of Planar Curves Surfaces And Nonplanar Space Curves Defined By Implicit Equations, With Applications To Edge And Range Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 1115–1138 (1991)
Proença, H., Alexandre, L.A.: UBIRIS: A Noisy Iris Image Database. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 970–977. Springer, Heidelberg (2005)
Dobeš, M., Machala, L.: UPOL Iris Image Database (2008). http://phoenix.inf.upol.cz/iris/
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
Abate, A.F., Nappi, M., Narducci, F., Ricciardi, S. (2014). Fast Iris Recognition on Smartphone by means of Spatial Histograms. In: Cantoni, V., Dimov, D., Tistarelli, M. (eds) Biometric Authentication. BIOMET 2014. Lecture Notes in Computer Science(), vol 8897. Springer, Cham. https://doi.org/10.1007/978-3-319-13386-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-13386-7_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13385-0
Online ISBN: 978-3-319-13386-7
eBook Packages: Computer ScienceComputer Science (R0)