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Cross-spectral Iris Recognition for Mobile Applications using High-quality Color Images

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
With the recent shift towards mobile computing, new challenges for biometric authentication appear on the horizon. This paper provides a comprehensive study of cross-spectral iris recognition in a scenario, in which high quality color images obtained with a mobile phone are used against enrollment images collected in typical, near-infrared setups. Grayscale conversion of the color images that employs selective RGB channel choice depending on the iris coloration is shown to improve the recognition accuracy for some combinations of eye colors and matching software, when compared to using the red channel only, with equal error rates driven down to as low as 2%. The authors are not aware of any other paper focusing on cross-spectral iris recognition is a scenario with near-infrared enrollment using a professional iris recognition setup and then a mobile-based verication employing color images.
Rocznik
Tom
Strony
91--97
Opis fizyczny
Bibliogr. 35 poz., rys., fot.
Twórcy
  • Biometrics Laboratory, Research and Academic Computer Network (NASK), Kolska st 12, 01-045 Warsaw, Poland
  • Institute of Control and Computation Engineering, Warsaw University of Technology, Nowowiejska st 15/19, 00-665 Warsaw, Poland
autor
  • Biometrics Laboratory, Research and Academic Computer Network (NASK), Kolska st 12, 01-045 Warsaw, Poland
Bibliografia
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  • [2] J. Daugman, “Biometric personal identification system based on iris analysis”, United States Patent, US 5291560, 1994.
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  • [5] Planet Biometrics, Microsoft brings iris recognition to the masses with new Lumia [Online]. Available: http://www.planetbiometrics.com/article-details/i/3606/desc/ microsoft-brings-iris-recognition-to-the-masses-with-new-lumia/ (accessed July 31, 2016).
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  • [7] C. Boyce, A. Ross, M. Monaco, L. Hornak, and X. Li, “Multispectral iris analysis: A preliminary study”, in Proc. Conf. Comp. Vision & Pattern Recogn. Worksh. CVPRW’06, New York, NY, USA, 2006 (doi: 10.1109/CUPRW.2006.141).
  • [8] J. H. Park and M. G. Kang, “Multispectral iris authentication system against counterfeit attack using gradient-based image fusion”, Optical Engin., vol. 46, no. 11, 2007 (doi: 10.1117/1.2802367).
  • [9] A. Ross, R. Pasula, and L. Hornak, “Iris recognition: On the segmentation of degraded images acquired in the visible wavelength”, in Proc. IEEE 3rd Int. Conf. Biometrics: Theory, Appl. and Syst. BTAS 2009, Washington, DC, USA, 2009.
  • [10] M. J. Burge and M. K. Monaco, “Multispectral iris fusion for enhancement, interoperability, and cross wavelength matching”, in Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, S. S. Shen and P. E. Lewis, Eds. Proc. of SPIE, vol. 7334, 73341D, 2009 (doi: 10.1117/12.819058).
  • [11] J. Zuo, F. Nicolo, and N. A. Schmid, “Cross spectral iris matching based on predictive image mapping”, in 4th IEEE Int. Conf. Biometrics: Theory, Appl. and Syst. BTAS 2010, Washington, DC, USA, 2010.
  • [12] M. Dobes, L. Machala, P. Tichavsky, and J. Pospisil, “Human eye iris recognition using the mutual information”, Optik, vol. 115, no. 9, pp. 399–404, 2004.
  • [13] H. Proença and L. A. Alexandre, “UBIRIS: A noisy iris image database”, Tech. Rep., ISBN: 972-99548-0-1, University of Beira Interior, Portugal, 2005.
  • [14] H. Proença, S. Filipe, R. Santos, J. Oliveira, and L. A. Alexandre, “The UBIRIS.v2: A database of visible wavelength iris images captured on-the-move and at-a-distance”, IEEE Trans. Pattern Anal. and Machine Intell., vol. 32, no. 8, pp. 1529–1535, 2010.
  • [15] M. Trokielewicz, “Iris recognition with a database of iris images obtained in visible light using smartphone camera”, in Proc. IEEE Int. Conf. on Ident., Secur. and Behavior Anal. ISBA 2016, Sendai, Japan, 2016.
  • [16] Warsaw-BioBase-Smartphone-Iris-v1.0 [Online]. Available: http://zbum.ia.pw.edu.pl/en/node/46
  • [17] H. Proença, “On the feasibility of the visible wavelength, at-adistance and on-the-move iris recognition”, in Proc. IEEE Symp. Series on Computat. Intell. in Biometr.: Theory, Algorithms, & Appl. SSCI 2009, Nashville, TN, USA, 2009, vol. 1, pp. 9–15.
  • [18] H. Proença, “Iris recognition: On the segmentation of degraded images acquired in the visible wavelength”, IEEE Trans. on Pattern Anal. & Mach. Intellig., vol. 32, no. 8l pp. 1502–1516, 2010.
  • [19] H. Proença, “Quality assessment of degraded iris images acquired in the visible wavelength”, IEEE Trans. Inform. Forens. and Secur., vol. 6, no. 1, pp. 82–95, 2011.
  • [20] G. Santos, M. V. Bernardo, H. Proenca, and P. T. Fiadeiro, “Iris recognition: Preliminary assessment about the discriminating capacity of visible wavelength data”, in Proc. 6th IEEE Int. Worksh. Multim. Inform. Process. and Retrieval MIPR 2010, Taichung, Taiwan China, 2010, pp. 324–329.
  • [21] P. Radu, K. Sirlantzis, G. Howells, S. Hoque, and F. Deravi, “A colour iris recognition system employing multiple classifier techniques”, Elec. Lett. Comp. Vision and Image Anal., vol. 12, no. 2, pp. 54–65, 2013.
  • [22] M. Frucci, C. Galdi, M. Nappi, D. Riccio, and G. Sanniti di Baja, “IDEM: Iris detection on mobile devices”, in Proc. 22nd Int. Conf. Pattern Recogn. ICPR 2014, Stockholm, Sweden, 2014.
  • [23] K. Raja, R. Raghavendra, F. Cheikh, B. Yang, and C. Busch, “Robust iris recognition using light field camera”, in The 7th Colour and Visual Comput. Symp. CVCS 2013, Gjøvik, Norway, 2013.
  • [24] K. Raja, R. Raghavendra, and C. Busch, “Iris imaging in visible spectrum using white LED”, in 7th IEEE Int. Conf. on Biometr.: Theory, Appl. and Syst. BTAS 2015, Arlington, VA, USA, 2015.
  • [25] K. Raja, R. Raghavendra, V. Vemuri, and C. Busch, “Smartphone based visible iris recognition using deep sparse filtering”, Pattern Recogn. Lett., vol. 57, pp. 33–42, 2014.
  • [26] K. B. Raja, R. Raghavendra, and C. Busch, “Smartphone based robust iris recognition in visible spectrum using clustered K-mean features”, in Proc. IEEE Worksh. Biometr. Measur. and Syst. for Secur. and Med. Appl. BioMS 2014, Rome, Italy, 2014, pp. 15–21.
  • [27] M. De Marsico, C. Galdi, M. Nappi, and D. Riccio, “FIRME: Face and iris recognition engagement”, Image and Vis. Comput., vol. 32, no. 12, pp. 1161–1172, 2014.
  • [28] M. Trokielewicz, E. Bartuzi, K. Michowska, A. Andrzejewska, and M. Selegrat, “Exploring the feasibility of iris recognition for visible spectrum iris images obtained using smartphone camera”, in Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2015, R. S. Romaniuk, Ed. Proc. of SPIE, vol. 9662, 2015 (doi: 10.1117/12.2205913).
  • [29] P. van Slembrouck, “Structural Eye Color is Amazing” [Online]. Available: http://medium.com/@ptvan/structural-eye-color-isamazing-24f47723bf9a (accessed Aug. 8, 2016).
  • [30] ISO/IEC 19794-6:2011. Information technology – Biometric data interchange formats – Part 6: Iris image data, 2011.
  • [31] Smart Sensors Ltd., MIRLIN SDK, version 2.23, 2013.
  • [32] D. M. Monro, S. Rakshit, and D. Zhang, “DCT-based iris recognition”, IEEE Trans. Pattern Anal. and Machine Intell., vol. 29, no. 4, pp. 586–595, 2007.
  • [33] IriTech Inc., IriCore Software Develope’s Manual, version 3.6, 2013 [Online]. Available: www.iritech.com/products/swoftware/iricoreeye-recognition-software
  • [34] Neurotechnology Company, VeriEye SDK, version 4.3 [Online]. Available: www.neurotechnology.com/verieye.html (accessed Aug. 11, 2015).
  • [35] G. Sutra, B. Dorizzi, S. Garcia-Salitcetti, and N. Othman, “A biometric reference system for iris. OSIRIS version 4.1 [Online]. Available: http://svnext.it-sudparis.eu/svnview2-eph/ref syst/iris osiris v4.1 (accessed Oct. 1, 2014).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-758e20f3-d6e0-41eb-8472-aa774add12c7
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