Learning Based Enhancement Model of Iris

Iris recognition is one of the most reliable personal identification methods. The potential requirement of obtaining high accuracy is that users supply iris images with good quality. It is thus necessary for an iris recognition system to operate the possibly blurred iris images due to less cooperation of users and camera with low resolution. This paper proposes a new algorithm for resolution enhancement of iris images captured by the low resolution camera in less cooperative situations. The prior probability relation between the information of different frequency bands of iris features useful for recognition is firstly learned. Then, it is incorporated into resolution enhancement algorithms to recover the lost information for the seriously blurred images. A large number of experiments on the CASIA iris database demonstrate the validity of the proposed approach.

[1]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Jan P. Allebach,et al.  Iterative reconstruction of bandlimited images from nonuniformly spaced samples , 1987 .

[3]  Michal Irani,et al.  Improving resolution by image registration , 1991, CVGIP Graph. Model. Image Process..

[4]  John Daugman,et al.  High Confidence Visual Recognition of Persons by a Test of Statistical Independence , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Tai Sing Lee,et al.  Image Representation Using 2D Gabor Wavelets , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Boualem Boashash,et al.  A human identification technique using images of the iris and wavelet transform , 1998, IEEE Trans. Signal Process..

[7]  Robert L. Stevenson,et al.  Simultaneous multi-frame MAP super-resolution video enhancement using spatio-temporal priors , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[8]  C. Stiller,et al.  Estimating motion in image sequences , 1999, IEEE Signal Process. Mag..

[9]  William T. Freeman,et al.  Learning low-level vision , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[10]  Takeo Kanade,et al.  Limits on super-resolution and how to break them , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[11]  Harry Shum,et al.  A two-step approach to hallucinating faces: global parametric model and local nonparametric model , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[12]  David Salesin,et al.  Image Analogies , 2001, SIGGRAPH.

[13]  Tieniu Tan,et al.  Iris recognition using circular symmetric filters , 2002, Object recognition supported by user interaction for service robots.

[14]  William T. Freeman,et al.  Example-Based Super-Resolution , 2002, IEEE Computer Graphics and Applications.

[15]  John Daugman,et al.  Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns , 2001, International Journal of Computer Vision.