Journal of Automation and Control
ISSN (Print): 2372-3033 ISSN (Online): 2372-3041 Website: https://www.sciepub.com/journal/automation Editor-in-chief: Santosh Nanda
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Journal of Automation and Control. 2014, 2(1), 33-38
DOI: 10.12691/automation-2-1-5
Open AccessArticle

Iris Verification and ANOVA for Iris Image Quality

Lidong Wang1,

1Department of Engineering Technology, Mississippi Valley State University, Itta Bena, USA

Pub. Date: March 04, 2014

Cite this paper:
Lidong Wang. Iris Verification and ANOVA for Iris Image Quality. Journal of Automation and Control. 2014; 2(1):33-38. doi: 10.12691/automation-2-1-5

Abstract

Iris recognition has been acknowledged as the most accurate method in biometrics that is one of main automated identification technologies. The iris image quality and iris verification of eyes in dark brown, regular brown, hazel, green, and blue was tested. The effects of eyeglasses and contact lenses on the iris image quality score and iris verification were investigated, respectively. The investigated results indicate that the iris verifications with eyeglasses or contact lenses can still be successful although both eyeglasses and contact lenses decrease iris image quality. Analysis of variance (ANOVA) for the iris image quality of three eye colors (dark brown, hazel, and blue) was conducted to study the difference in the image quality due to the eye colors. The ANOVA results show there is no significant difference in the iris image quality of eyes in dark brown, hazel, and blue at the 0.05 level of significance.

Keywords:
iris image quality iris verification biometrics automated identification ANOVA level of significance

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References:

[1]  Devireddy, Srinivasa Kumar. “An Accurate Human Identification Through Iris Recognition.” Computer Science and Telecommunications, vol. 23, no. 6, 2009, pp. 22-29.
 
[2]  Ezhilarasan, M., Jacthish, R., Subramanian, Ganabathy K.S. and Umapathy, R. “Iris Recognition Based on Its Texture Patterns.” International Journal on Computer Science and Engineering, vol. 2, no. 9, 2010, pp. 3071-3074.
 
[3]  Lee, J-C, Su, Y., Tu, T-M. and Chang C-P. “A novel approach to image quality assessment in iris recognition systems.” The Imaging Science Journal, vol. 58, 2010, pp. 136-145.
 
[4]  Wei, Zhuoshi, Tan, Tieniu, Sun, Zhenan and Cui, Jiali. “Robust and Fast Assessment of Iris Image Quality.” Proceedings of the 2006 International Conference on Advances in Biometrics, Springer-Verlag Berlin, Heidelberg, 2006, pp. 464-471.
 
[5]  Cambier, Jim. “Iris Image Quality Metrics”, Company Confidential and Proprietary, Technical Report, November, 2007.
 
[6]  Kalka, Nathan D., Zuo, Jinyu, Schmid, Natalia A. Cukic, Bojan. “Image quality assessment for iris biometric.” Proc. of 2006 SPIE Conf. on Biometric Technology for Human Identification III, Orlando, FL, USA, April 17-18, 2006, vol. 6202, pp. pp. 445-452.
 
[7]  Bowyer, Kevin W., Hollingsworth, Karen and Flynn, Patrick J. “Image Understanding for Iris Biometrics: A Survey.” Computer Vision and Image Understanding, vol. 110, no. 2, May 2008, pp. 281-307.
 
[8]  Kang, Byung Jun, Park, Kang Ryoung. “A new multi-unit iris authentication based on quality assessment and score level fusion for mobile phones.” Machine Vision and Applications, no. 21, 2010, pp. 541-553.
 
[9]  Li, Xingguang, Sun, Zhenan, Tan, Tieniu. “Comprehensive assessment of iris image quality.” 18th IEEE International Conference on Image Processing, Brussels, Belgium, September 11-14, 2011, pp. 3117-3120.
 
[10]  McConnon, G. et al. “A Survey of Point-Source Specular Reflections in Noisy Iris Images.” International Conference on Emerging Security Technologies, Canterbury, United Kingdom, September 6-7, 2010, pp. 13-17.
 
[11]  Chaskar, U. M., Sutaone, M. S., Shah, N. S., Jaison. T. “Iris Image Quality Assessment for Biometric Application.” International Journal of Computer Science Issues, vol. 9, no. 3, May 2012, pp. 474-478.
 
[12]  Proenca, Hugo. “An iris recognition approach through structural pattern analysis methods.” Expert Systems, February 2010, vol. 27, no. 1, pp. 6-16.
 
[13]  Kang, Byung Jun, Park, Kang Ryoung. “A Study on Restoration of Iris Images with Motion-and-Optical Blur on Mobile Iris Recognition Devices.” International Journal of Imaging Systems & Technology, vol. 19, 2009, pp. 323-331.
 
[14]  LG Electronics - Iris Technology Division. IrisAccessTM Software User Manual, Version 3.00, New Jersey, USA, December13, 2007.
 
[15]  LG Electronics - Iris Technology Division. IrisAccessTM 4000 Hardware Manual, New Jersey, USA, 2008.
 
[16]  Wang, Lidong. “Iris Image Quality Testing and Iris Verification.” International Journal of Electrical and Computer Engineering, vol. 3, no. 4, 2013, pp. 1-7.
 
[17]  Wasserman, Philip D. “Digital Image Quality for Iris Recognition.” Biometric Image Quality Workshop, National Institute of Standards and Technology, USA, March 8-9, 2006.
 
[18]  Fu, Jian, Caulfield, H. John, Yoo, Seong-Moo, Atluri, Venkata, “Use of Artificial Color filtering to improve iris recognition and searching.” Pattern Recognition Letters, vol. 26, 2005, pp. 2244-2251.
 
[19]  Wang, Changyu, Song, Shangling. “An iris recognition algorithm based on fractal dimension.” Acta Automatica Sinica, vol. 33, no. 7, 2007, pp.608-702.
 
[20]  Birgale, L., Kokare, M. “Comparison of Color and Texture for Iris Recognition.” International Journal of Pattern Recognition and Artificial Intelligence, vol. 26, no. 3, 1256007, 2012.
 
[21]  Monaco, Matthew K. “Effect of Eye Color on Iris Recognition.” Thesis for the degree of Master of Science, Virginia University, 2007.
 
[22]  Smith, Kelly N. “Analysis of Pigmentation and Wavefront Coding(TM) Acquisition in Iris Recognition.” January 1, 2007, ProQuest Publisher.
 
[23]  Freund, J. E. and Perles, B. M. Statistics: A First Course. (8th Ed.), Pearson Prentice Hall, New Jersey, 2004.
 
[24]  Merrington M. and Thompson, C. M. “Tables of percentage points of the inverted beta (F) distribution.” Biometrika, vol. 33, 1943.
 
[25]  Sulem, P. et al. “Genetic determinants of hair, eye and skin pigmentation in Europeans.” Nature Genetics, vol. 39, Dec. 2007, pp. 1443-1452.