Iris Individuality: A Partial Iris Model
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
Graph matching iris image blocks with local binary pattern
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Extracting and combining multimodal directional iris features
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
IEEE Transactions on Circuits and Systems for Video Technology
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The most common iris biometric algorithm represents the texture of an iris using a binary iris code. Not all bits in an iris code are of equal value. A bit is deemed fragile if it varies in value across iris codes created from different images of the same iris. Previous research has shown that iris recognition performance can be improved by masking these fragile bits. Rather than ignoring fragile bits completely, we consider what beneficial information can be obtained from the fragile bits. We find that the locations of fragile bits tend to be consistent across different iris codes of the same eye. We present a metric, called the fragile bit distance, which quantitatively measures the coincidence of the fragile bit patterns in two iris codes. We find that score-fusion of fragile bit distance and Hamming distance works better for recognition than Hamming distance alone. This is the first and only work that we are aware of to use the coincidence of fragile bit locations to improve the accuracy of matches.