A novel iris segmentation using radial-suppression edge detection
Signal Processing
Low-complexity iris coding and recognition based on directionlets
IEEE Transactions on Information Forensics and Security
Corrections to "A selective feature information approach for iris image-quality measure"
IEEE Transactions on Information Forensics and Security
Feature correlation evaluation approach for iris feature quality measure
Signal Processing
Iris recognition using signal-level fusion of frames from video
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
A comprehensive approach for skin recognition
International Journal of Biometrics
Speed-up multi-stage non-cooperative iris recognition
International Journal of Biometrics
A comprehensive approach for sclera image quality measure
International Journal of Biometrics
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Poor quality images can significantly affect the accuracy of iris-recognition systems because they do not have enough feature information. However, existing quality measures have focused on parameters or factors other than feature information. The quality of feature available for measure is a combination of the distinctiveness of the iris region and the amount of iris region available. Some irises may only have a small area of changing patterns. Due to this, the proposed approach automatically selects the portions of the iris with the most distinguishable changing patterns to measure the feature information. The combination of occlusion and dilation determines the amount of iris region available and is considered in the proposed quality measure. The quality score is the fused result of the feature information score, the occlusion score, and the dilation score. The relationship between the quality score and recognition accuracy is evaluated using 2-D Gabor and 1-D Log-Gabor wavelet approaches and validated using a diverse data set. In addition, the proposed method is compared with the convolution matrix, spectrum energy, and Mexican hat wavelet methods. These three methods represent a variety of approaches for iris-quality measure. The experimental results show that the proposed quality score is highly correlated with the recognition accuracy and is capable of predicting the recognition results.