An Evaluation of Multimodal 2D+3D Face Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experiments with an Improved Iris Segmentation Algorithm
AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
Preliminary Face Recognition Grand Challenge Results
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Iris Recognition Based on DLDA
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Overview of the Multiple Biometrics Grand Challenge
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Iris recognition with support vector machines
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Specific texture analysis for iris recognition
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Performance analysis of iris-based identification system at the matching score level
IEEE Transactions on Information Forensics and Security
Efficient iris recognition by characterizing key local variations
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
Robust mean super-resolution for less cooperative NIR iris recognition at a distance and on the move
Proceedings of the 2010 Symposium on Information and Communication Technology
Super resolution reconstruction and recognition for iris image sequence
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
A review of information fusion techniques employed in iris recognition systems
International Journal of Advanced Intelligence Paradigms
Feature-domain super-resolution for iris recognition
Computer Vision and Image Understanding
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We take advantage of the temporal continuity in an iris video to improve matching performance using signal-level fusion. From multiple frames of an iris video, we create a single average image. Our signal-level fusion method performs better than methods based on single still images, and better than previously published multi-gallery score-fusion methods. We compare our signal fusion method with another new method: a multi-gallery, multi-probe score fusion method. Between these two new methods, the multi-gallery, multi-probe score fusion has slightly better recognition performance, while the signal fusion has significant advantages in memory and computation requirements.