Browse > Article
http://dx.doi.org/10.3837/tiis.2010.04.003

Super-Resolution Iris Image Restoration using Single Image for Iris Recognition  

Shin, Kwang-Yong (Division of Electronics and Electrical Engineering, Dongguk University, Biometrics Engineering Research Center)
Kang, Byung-Jun (ETRI (Electronics and Telecommunications Research Institute))
Park, Kang-Ryoung (Division of Electronics and Electrical Engineering, Dongguk University, Biometrics Engineering Research Center)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.4, no.2, 2010 , pp. 117-137 More about this Journal
Abstract
Iris recognition is a biometric technique which uses unique iris patterns between the pupil and sclera. The advantage of iris recognition lies in high recognition accuracy; however, for good performance, it requires the diameter of the iris to be greater than 200 pixels in an input image. So, a conventional iris system uses a camera with a costly and bulky zoom lens. To overcome this problem, we propose a new method to restore a low resolution iris image into a high resolution image using a single image. This study has three novelties compared to previous works: (i) To obtain a high resolution iris image, we only use a single iris image. This can solve the problems of conventional restoration methods with multiple images, which need considerable processing time for image capturing and registration. (ii) By using bilinear interpolation and a constrained least squares (CLS) filter based on the degradation model, we obtain a high resolution iris image with high recognition performance at fast speed. (iii) We select the optimized parameters of the CLS filter and degradation model according to the zoom factor of the image in terms of recognition accuracy. Experimental results showed that the accuracy of iris recognition was enhanced using the proposed method.
Keywords
Super-Resolution iris image; iris recognition; CLS filter;
Citations & Related Records

Times Cited By Web Of Science : 2  (Related Records In Web of Science)
Times Cited By SCOPUS : 2
연도 인용수 순위
1 CASIA ver. 3. http://www.cbsr.ia.ac.cn/IrisDatabase.htm (accessed on January 4, 2010)
2 R. Lokhande, K. V. Arya, and P. Gupta, "Identification of Parameters and Restoration of Motion Blurred Images," in Proc. of the 2006 ACM Symposium on Applied Computing, pp.301-305, 2006.
3 B. J. Kang, and K. R. Park, "Real-time Image Restoration for Iris Recognition Systems," IEEE Trans. on Systems, vol.37, no.6, pp.1555-1566, 2007.
4 Hyun-Ae Park, and Kang Ryoung Park, "Iris Recognition Based on Score Level Fusion by Using SVM," Pattern Recognition Letters, vol.28, no.15, pp.2019-2028, 2007.   DOI   ScienceOn
5 B. J. Kang, and K. R. Park, "A Robust Eyelash Detection Based on Iris Focus Assessment," Pattern Recognition Letters, vol.28, no.13, pp.1630-1639, 2007.   DOI   ScienceOn
6 S. Dai, M. Han, Y. Wu, and Y. Gong, "Bilateral Back-Projection for Single Image Super Resolution" in Proc. of Int. Conf. on Multimedia and Expo, pp.1039-1042, 2007.
7 R. C. Gonzalez, and R. E. Woods, Digital Image Processing, 2/E, Prentice Hall, 2002.
8 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, no.11, pp.1698-1704, 2008.   DOI   ScienceOn
9 M. Manry, and J. Aggarwal, "The Measurement of Phase Distortion Due to Filtering in Digital Pictures," IEEE Trans. on Acoust., vol.ASSP-25, pp.534-541, 1977.
10 D. S. Jeong, J. W. Hwang, B. J. Kang, K. R. Park, C. S. Won, D. K. Park, and J. Kim, "A New Iris Segmentation Method for Non-ideal Iris Images," Image and Vision Computing, 2010, accepted for publication.
11 K. Y. Shin, B. J. Kang, and K. R. Park, "A Study on the Restoration of a Low-Resoltuion Iris Image into a High-Resolution One Based on Multiple Multi-Layered Perceptrons," Korea Multimedia Society, Mar 2010, accepted for publication.
12 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," in Proc. of SNPD 2006, 2006.
13 H. Greenspan, G. Oz, N. Kiryati and S. Peled, "MRI Inter-Slice Reconstruction Using Super Resolution," Magnetic Resonance Imaging, vol.20, pp.437-446, 2002.   DOI   ScienceOn
14 S. W. Lee, J. Y. Park and S.W. Lee, "Low Resolution Face Recognition Based on Support Vector Data Description," Pattern Recognition, vol.39, no.9, pp. 1809-1812, 2006.   DOI   ScienceOn
15 Jiali. Cui, Yunhong Wang, Junzhou. Huang, Tieniu Tan, and Zhenan Sun, "An Iris Image Synthesis Method based on PCA and Super-resolution," in Proc. of 17th Int Conf. on Pattern Recognition, vo.4, pp.471-474, 2004.
16 K. Y. Shin, B. J. Kang, and K. R. Park, "Super-Resolution Method Based on Multiple Multi-Layer Perceptrons for Iris Recognition," in Proc. of the 4th Int Conf. on Ubiquitous Information Technologies & Applications, 2009.
17 D. Capel and A. Zisserman, "Computer Vision Applied to Super-resolution," IEEE Signal Processing Magazine 20, pp.75-86, 2003.
18 Liyakathunisa, and V.K. Anantha Shayana "Super Resolution Blind Restoration of Noisy, Blurred and Aliased Low Resolution Images under Compression," in Proc. of Int Conf of. on Information Systems, pp.61-66, 2007.
19 G. Fahmy, "Super-resolution Construction of IRIS Images from a Visual Low Resolution Face Video," In Proc. of International Symposium on Signal Processing and its Applications, pp.1-4, 2007.
20 R. Barnard et al., "High-Resolution Iris Image Reconstruction from Low-Resolution Imagery", in Proc. of the SPIE, Advanced Signal Processing Algorithms, Architectures, and Implementations XVI, vol. 6313, pp. D1-D13, 2006.
21 F. Lin, C. Fookes, V. Chandran, and S. Sridharan, "Investigation into Optical Flow Super-Resolution for Surveillance Applications," in Proc. of APRS Workshop on Digital Image Computing, pp.73-78, 2005.
22 S. C. Park, M. K. Park, and M. G. Kang, "Super-resolution Image Reconstruction: a Technical Overview," IEEE Signal Processing Magazine, vol.20, no.3, pp.21-36, May 2003.   DOI   ScienceOn
23 BM-ET 200. http://www.panasonic.com/business/security/biometrics.asp (accessed on January 4, 2010).
24 Frederick W. Wheeler, A. G. Amitha Perera, Gil Abramovich, Bing Yu, and Peter H. Tu, "Stand-off Iris Recognition System," in Proc. 2nd Int IEEE Conf of. On Biometrics:Theory, Applications and Systems, pp.1-7, 2008.
25 Wenbo Dong, Zhenan Sun, Tieniu Tan, and Xianchao Qiu, "Self-adaptive Iris Image Acquisition System," in Proc. of the SPIE Biometric Technology for Human Identification, vol.6944, pp.6-14, 2008.
26 Soweon Yoon, Ho Gi Jung, Kang Ryoung Park and Jaihie Kim, "Non-intrusive Iris Image Acquisition System Based on a Pan-Tilt-Zoom Camera and Light Stripe Projection," Optical Engineering, vol.48, no.3, pp.137202-1 - 137202-15, 2009.
27 R. P. Wildes, "Iris Recognition," Biometric Systemsm, pp. 63-95, 2005.
28 Information Technology, "Biometric Data Interchange Formats - Iris Image Data," ISO/IEC 19794-6, 2005.
29 IrisAccess 4000. http://www.lgiris.com/ps/products/index.htm (accessed on January 4, 2010).
30 J. R. Matey, O. Naroditsky, K. Hanna, R. Kolczynski, D. LoIacono, S. Mangru, M. Tinker, T. Zappia, and W.Y. Zhao, "Iris on the Move: Acquisition of Images for Iris Recognition in Less Constrained Environments," Proceedings of the IEEE, vol.94, pp.1936-1946, 2006.   DOI
31 IrisPass-M. http://www.oki.com/jp/FSC/iris/en/index.html (accessed on January 4, 2010).
32 J. G. Daugman, "How Iris Recognition Works," IEEE Trans. on Circuits and Systems for Video Technology, vol.14, no.1, pp.21-30, 2004.   DOI   ScienceOn
33 J. G. Daugman, "Demodulation by Complex-valued Wavelets for Stochastic Pattern Recognition," International Journal of Wavelets, vol.1, no.1, pp.1-17, 2003.
34 International Biometrics Group, Independent Testing of Iris Recognition Technology Final Report, 2005. http://www.biometricscatalog.org/document_area/default.aspx (accessed on January 4, 2010).
35 R. P. Wildes, "Automated Iris Recognition: An Emerging Biometric Technology," Proceedings of the IEEE, vol.85, no.9, pp.1348-1363, 1997.   DOI   ScienceOn
36 J. G. Daugman, "High Confidence Visual Recognition of Personals by a Test of Statistical Independence," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.15, no.11, pp. 1148-1160, 1993.   DOI   ScienceOn