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A novel portable iris recognition system and usability evaluation

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  • Robotics and Automation
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Abstract

We propose a new portable iris recognition system. Because existing portable iris systems use customized embedded processing units, they are limited in ability to expand to other applications, and they have low processing power. To overcome such problems, we propose a new portable iris recognition system consisting of a conventional ultra-mobile personal computer (UMPC), a small universal serial bus (USB) iris camera, and near-infrared (NIR) light illuminators. In general, portable iris systems produce considerable optical blurring. Although auto-focusing motor-driven lenses can be used to overcome it, they are too bulky to be used in a small-sized portable iris system. Therefore, we adopt an iris image restoration algorithm which performs at real-time speed. And by using a conventional UMPC as a processing unit, our portable iris system is more extensible than previous systems. In general, the performance of iris recognition has been mainly evaluated based on the quantitative metrics such as EER (Equal Error Rate), ROC (Receiver Operational Characteristics) curve or recognition time. We propose a new performance measuring method based on qualitative metrics. That is usability evaluation including user acceptance, convenience, satisfaction and resistance.

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References

  1. J. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 15, pp. 1148–1161, 1993.

    Article  Google Scholar 

  2. J. C. Yang and D. S. Park, “Fingerprint verification based on invariant moment features and nonlinear BPNN,” International Journal of Control, Automation, and Systems, vol. 6, no. 6, pp. 800–808, 2008.

    Google Scholar 

  3. H. Kim, J. Choi, and M. Kim, “Human-robot interaction in real environments by audio-visual integration,” International Journal of Control, Automation, and Systems, vol. 5, no. 1, pp. 61–69, 2007.

    Google Scholar 

  4. H. Lee, S. Hong, I. F. Nizami, and E. Kim, “A noise robust gait representation: motion energy image,” International Journal of Control, Automation, and Systems, vol. 7, no. 4, pp. 638–643, 2009.

    Article  Google Scholar 

  5. IrisAccess 3000 & 4000, http://www.lgiris.com (accessed on August 17, 2009).

  6. IrisPass-M, http://www.oki.com/jp/FSC/iris/en/(accessed on August 17, 2009).

  7. BM-ET 200, http://www.panasonic.com/business/ medicalvideo/biometrics.asp (accessed on August 17, 2009).

  8. IrisPass-H, http://www.pro-4-pro.com/media/downloads/product/9354428_1_p4p0804.pdf (accessed on August 17, 2009).

  9. PierTM 2.4, http://www.l1id.com/pages/48-pier-2-4 (accessed on August 17, 2009). [10]_HIIDETM 4, http://www.l1id.com/pages/47-hiideseries-4 (accessed on August 17, 2009).

  10. “U.S. is building database on Iraqis — Biometrics key part of tracking suspects,” USA Today, July 13, 2007.

  11. VGN-UX17LP, http://www.funponsel.com/blog/ computers/sony-vaio-vgn-ux17lp-mobile-pocket-pc. html (accessed on August 17, 2009).

  12. International Biometrics Group, “Independent testing of iris recognition technology (final report),” Rep. NBCHC030114/0002, May 2005, study commissioned by the U. S. Dept. Homeland Security.

  13. T. Mansfield, G. Kelly, D. Chandler, and J. Kane, “Biometric product testing final report,” http://www.cesg.gov.uk/policy_technologies/biometrics/media/biometrictestreportpt1.pdf (accessed on August 17, 2009).

  14. J. R. Matey, O. Naroditsky, K. Hanna, R. Kolczynski, D. LoIacono, S. Mangru, M. Tinker, T. M. Zappia, and W. Y. Zhao, “Iris on the move: acquisition of images for iris recognition in less constrained environments,” Proc. of IEEE, vol. 94, pp. 1936–1947, 2006.

    Article  Google Scholar 

  15. ISO/IEC 19794-6, “Information technology. biometric data interchange formats — iris image data,” 2005.

  16. J. Daugman, “Demodulation by complex-valued wavelets for stochastic pattern recognition,” International Journal of Wavelets, Multi-resolution and Information Processing, vol. 1, pp. 1–17, 2003.

    Article  MATH  Google Scholar 

  17. J. Daugman, “The Importance of being random: statistical principles of iris recognition,” Pattern Recognition, vol. 36, pp. 279–291, 2003.

    Article  Google Scholar 

  18. J. Daugman, “How iris recognition works,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 14, pp. 21–30, 2004.

    Article  Google Scholar 

  19. TLV of eye safety from ACGIH, http://www.acgih. org (accessed on August 17, 2009).

  20. V. Ekaterina et al., “Estimation of melanin content in iris of human eye,” Proc. of SPIE, vol. 5688, pp. 302–311, 2005.

    Article  Google Scholar 

  21. A. Vogel et al., “Optical properties of human sclera, and their consequences for transscleral laser applications,” Lasers in Surgery and Medicine, vol. 11, no. 4, pp. 331–340, 1991.

    Article  Google Scholar 

  22. J. van der Gracht, V. P. Pauca, H. Setty, R. Narayanswamy, R. J. Plemmons, S. Prasad, and T. Torgersen, “Iris recognition with enhanced depthof-field image acquisition,” Proc. of SPIE, vol. 5438, pp. 120–129, 2004.

    Article  Google Scholar 

  23. B. J. Kang and K. R. Park, “Real-time image restoration for iris recognition systems,” IEEE Trans on Systems, Man and Cybernetics — Part B, vol. 37, no. 6, pp. 1–13, 2007.

    Google Scholar 

  24. D. H. Cho, K. R. Park, D. W. Rhee, Y. G. Kim, and J. H. Yang, “Pupil and iris localization for iris recognition in mobile phones,” Proc. of the SNPD, Las Vegas, USA, 2006.

  25. K. R. Park et al., “A Study on iris localization and recognition on mobile phone,” EURASIP Journal on Advances in Signal Processing, vol. 2008, pp. 1–12, 2007.

    Article  Google Scholar 

  26. Y. K. Jang, B. J. Kang, and K. R. Park, “A study on eyelid localization considering image focus for iris recognition,” Pattern Recognition Letters, vol. 29,issue 11, pp. 1698–1704, 2008.

    Article  Google Scholar 

  27. B. J. Kang and K. R. Park, “A robust eyelash detection based on iris focus assessment,” Pattern Recognition Letters, vol. 28,issue 13, pp. 1630–1639, 2007.

    Article  Google Scholar 

  28. H. A. Park and K. R. Park, “Iris recognition based on score level fusion by using SVM,” Pattern Recognition Letters, vol. 28,issue 15, pp. 2019–2028, 2007.

    Article  Google Scholar 

  29. J. Nielsen, Usability Engineering, Morgan Kaufmann, 1994.

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Authors and Affiliations

  1. Department of Information and Communication, Gwangju Institute of Science and Technology (GIST), 1, Oryong-dong, Buk-gu, Gwangju, Korea

    Youngkyoon Jang

  2. Electronics and Telecommunications Research Institute (ETRI), 138 Gajeongno, Yueseong-gu, Daejeon, Korea

    Byung Jun Kang

  3. Dept. of Electronics Engineering, Dongguk University, Pildong-3ga, Jung-gu, Seoul, Korea

    Kang Ryoung Park

Authors
  1. Youngkyoon Jang
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  2. Byung Jun Kang
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  3. Kang Ryoung Park
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Corresponding author

Correspondence to Kang Ryoung Park.

Additional information

Recommended by Editorial Board member Sooyong Lee under the direction of Editor Young-Hoon Joo. This work was supported by the Korea Science and Engineering Foundation (KOSEF) through the Biometrics Engineering Research Center (BERC) at Yonsei University [R112002105070020 (2009)].

Young Kyoon Jang received the B.S. degree in Software from Sangmyung University in 2007. He has been the combined course of M.S. and Ph.D. from GIST since 2008. His research interests include biometrics systems, pattern recognition, and digital image processing.

Byung Jun Kang received the B.S. degree in Software in 2004 and the M.S. and Ph.D. degrees in Computer Science in 2006 and 2009, from Sangmyung University. He has been a senior researcher in Electronics and Telecommunications Research Institute (ETRI). His research interests include iris image restoration, iris recognition, biometrics, digital image processing, and pattern recognition.

Kang Ryoung Park received the B.S. and M.S. degrees in Electronic Engineering from Yonsei University in 1994 and 1996, respectively. He received the Ph.D. degree in Electrical and Computer Engineering from Yonsei University in 2000. He has been an associate professor in the Department of Electronics Engineering at Dongguk University since March 2008. And he is also a research member in the Biometrics Engineering Research Center (BERC). His research interests include face, iris, and finger vein image processing, and gaze detection.

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Jang, Y., Kang, B.J. & Park, K.R. A novel portable iris recognition system and usability evaluation. Int. J. Control Autom. Syst. 8, 91–98 (2010). https://doi.org/10.1007/s12555-010-0112-0

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  • DOI: https://doi.org/10.1007/s12555-010-0112-0

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