High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Personal Identification Based on Iris Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture detection for segmentation of iris images
SAICSIT '05 Proceedings of the 2005 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries
Iris Recognition Algorithm Using Modified Log-Gabor Filters
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
A robust eyelash detection based on iris focus assessment
Pattern Recognition Letters
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
ICCI '06 Proceedings of the 2006 5th IEEE International Conference on Cognitive Informatics - Volume 02
A human identification technique using images of the iris andwavelet transform
IEEE Transactions on Signal Processing
IEEE Transactions on Circuits and Systems for Video Technology
Unsupervised range-constrained thresholding
Pattern Recognition Letters
Robust iris verification based on local and global variations
EURASIP Journal on Advances in Signal Processing
Image bilevel thresholding based on stable transition region set
Digital Signal Processing
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Eyelids and eyelashes occluding the iris region are noise factors that degrade the performance of iris recognition. If they are incorrectly classified as an iris region, the false iris region information decreases the recognition rate. Thus, reliable detection of eyelids and eyelashes is required to improve the performance of iris recognition. In this paper, we propose an automatic eyelid and eyelash detection method based on the parabolic Hough model and Otsu's thresholding method. By applying the parabolic Hough transform to the normalized iris image, rather than to the original image, we reduce the dimension of the parameter space and limit the parameter search range, decreasing the computational load. In addition, for automatically separating the eyelash region we apply Otsu's method to the proposed feature that is obtained by combining the intensity and local standard deviation values. The proposed method is applied to the CASIA version 3 database and the performance of the proposed and six existing methods is assessed in terms of the decidability, equal error rate, and detection error trade-off curve. In terms of these performance measures, the proposed method shows the better performance than conventional methods.