High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reliable and Fast Eye Finding in Close-up Images
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
ACM SIGGRAPH 2004 Papers
Iris Recognition Using Wavelet Features
Journal of VLSI Signal Processing Systems
A New Iris Segmentation Method for Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
UBIRIS: a noisy iris image database
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
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
Robust iris verification based on local and global variations
EURASIP Journal on Advances in 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
Multi-stage visible wavelength and near infrared iris segmentation framework
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Effective elliptic fitting for iris normalization
Computer Vision and Image Understanding
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Iris recognition has been widely used in several scenarios with very satisfactory results. As it is one of the earliest stages, the image segmentation is in the basis of the process and plays a crucial role in the success of the recognition task. In this paper we analyze the relationship between the accuracy of the iris segmentation process and the error rates of three typical iris recognition methods. We selected 5000 images of the UBIRIS, CASIA and ICE databases that the used segmentation algorithm can accurately segment and artificially simulated four types of segmentation inaccuracies. The obtained results allowed us to conclude about a strong relationship between translational segmentation inaccuracies - that lead to errors in phase - and the recognition error rates.