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
Personal Identification Based on Iris Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image region entropy: a measure of "visualness" of web images associated with one concept
Proceedings of the 13th annual ACM international conference on Multimedia
Toward Noncooperative Iris Recognition: A Classification Approach Using Multiple Signatures
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
IEEE Transactions on Circuits and Systems for Video Technology
Noisy Iris Verification: A Modified Version of Local Intensity Variation Method
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Robust iris verification based on local and global variations
EURASIP Journal on Advances in Signal Processing
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The iris is currently accepted as one of the most accurate traits for biometric purposes. However, for the sake of accuracy, iris recognition systems rely on good quality images and significantly deteriorate their results when images contain large noisy regions, either due to iris obstructions (eyelids or eyelashes) or reflections (specular or lighting). In this paper we propose an entropy-based iris coding strategy that constructs an unidimensional signal from overlapped angular patches of normalized iris images. Further, in the comparison between biometric signatures we exclusively take into account signatures' segments of varying dimension. The hope is to avoid the comparison between components corrupted by noise and achieve accurate recognition, even on highly noisy images. Our experiments were performed in three widely used iris image databases (third version of CASIA, ICE and UBIRIS) and led us to observe that our proposal significantly decreases the error rates in the recognition of noisy iris images.