Personal Identification Based on Iris Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A human identification technique using images of the iris andwavelet transform
IEEE Transactions on Signal Processing
Efficient iris recognition by characterizing key local variations
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
Image processing in personal identification
SIP'10 Proceedings of the 9th WSEAS international conference on Signal processing
Enhancing iris matching using levenshtein distance with alignment constraints
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Nonlinear Iris deformation correction based on Gaussian model
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
| Hi-index | 0.00 |
Iris-based biometric recognition outperforms other biometric methods in terms of accuracy. In this paper an iris normalization model for iris recognition is proposed, which combines linear and non-linear methods to unwrap the iris region. First, non-linearly transform all iris patterns to a reference annular zone with a predefined λ, which is the ratio of the radii of inner and outer boundaries of the iris. Then linearly unwrap this reference annular zone to a fix-sized rectangle block for subsequence processing. Our iris normalization model is illuminated by the ‘minimum-wear-and-tear' meshwork of the iris and it is simplified for iris recognition. This model explicitly shows the non-linear property of iris deformation when pupil size changes. And experiments show that it does better than the over-simplified linear normalization model and will improve the iris recognition performance.