Iris Image Evaluation for Non-cooperative Biometric Iris Recognition System

During video acquisition of an automatic non-cooperative biometric iris recognition system, not all the iris images obtained from the video sequence are suitable for recognition. Hence, it is important to acquire high quality iris images and quickly identify them in order to eliminate the poor quality ones (mostly defocused images) before the subsequent processing. In this paper, we present the results of a comparative analysis of four methods for iris image quality assessment to select clear images in the video sequence. The goal is to provide a solid analytic ground to underscore the strengths and weaknesses of the most widely implemented methods for iris image quality assessment. The methods are compared based on their robustness to different types of iris images and the computational effort they require. The experiments with the built database (100 videos from MBGC v2) demonstrate that the best performance scores are generated by the kernel proposed by Kang & Park. The FAR and FRR obtained are 1.6% and 2.3% respectively.

[1]  Josef Kittler,et al.  Audio- and Video-Based Biometric Person Authentication, 5th International Conference, AVBPA 2005, Hilton Rye Town, NY, USA, July 20-22, 2005, Proceedings , 2005, AVBPA.

[2]  Dexin Zhang,et al.  Personal Identification Based on Iris Texture Analysis , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Natalia A. Schmid,et al.  Image quality assessment for iris biometric , 2006, SPIE Defense + Commercial Sensing.

[4]  Jay Martin Tenenbaum,et al.  Accommodation in computer vision , 1971 .

[5]  Shree K. Nayar,et al.  Shape from Focus , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Yuanning Liu,et al.  A quality evaluation method of iris images sequence based on wavelet coefficients in "region of interest" , 2004, The Fourth International Conference onComputer and Information Technology, 2004. CIT '04..

[7]  Tieniu Tan,et al.  Robust and Fast Assessment of Iris Image Quality , 2006, ICB.

[8]  Marco Gamassi,et al.  Quality assessment of biometric systems: a comprehensive perspective based on accuracy and performance measurement , 2005, IEEE Transactions on Instrumentation and Measurement.

[9]  李幼升,et al.  Ph , 1989 .

[10]  Yingzi Du,et al.  Information distance-based selective feature clarity measure for iris recognition , 2007, Electronic Imaging.

[11]  John Daugman,et al.  How iris recognition works , 2002, IEEE Transactions on Circuits and Systems for Video Technology.

[12]  Patrick J. Flynn,et al.  Image understanding for iris biometrics: A survey , 2008, Comput. Vis. Image Underst..

[13]  Kang Ryoung Park,et al.  A Study on Iris Image Restoration , 2005, AVBPA.

[14]  Anil K. Jain,et al.  Localized Iris Image Quality Using 2-D Wavelets , 2006, ICB.

[15]  Natalia A. Schmid,et al.  Global and local quality measures for NIR iris video , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[16]  Libor Masek,et al.  Recognition of Human Iris Patterns for Biometric Identification , 2003 .

[17]  Zhengxuan Wang,et al.  Genetic Algorithm Based Parameter Identification of Defocused Image , 2008, 2008 International Conference on Computer Science and Information Technology.

[18]  Xu Jin Iris image quality evaluation method based on wavelet packet decomposition , 2003 .

[19]  Stan Z. Li,et al.  Advances in Biometrics, International Conference, ICB 2007, Seoul, Korea, August 27-29, 2007, Proceedings , 2007, ICB.