An Automated Video-Based System for Iris Recognition
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Robust and accurate iris segmentation in very noisy iris images
Image and Vision Computing
Detection of the foveal avascular zone on retinal angiograms using Markov random fields
Digital Signal Processing
Video pupil tracking for iris based identification
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
A study on iris image restoration
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
IEEE Transactions on Circuits and Systems for Video Technology
Quality-Driven Super-Resolution for Less Constrained Iris Recognition at a Distance and on the Move
IEEE Transactions on Information Forensics and Security
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This work deals with dynamic iris biometry using video, which is increasingly gaining interest for its flexibility in the framework of biometric portals. We propose several improvements for "real-time" dynamic iris biometry in order to build gradually an iris code of high quality by selecting on-the-fly the best iris images as they appear during acquisition. In particular, tracking is performed using an optimally-tuned Kalman's filter, i.e. a Kalman's filter with state and observation matrices specifically learned to follow the movement of a pupil. Experiments on four videos acquired with an IR-sensitive low-cost webcam show reduced computation time with a slight but significant gain in accuracy when compared to the classical Kalman tracker. The second main contribution is to combine iris codes of images within the video stream providing the "best quality" iris texture. The so-obtained fuzzy iris codes clearly exhibit areas with high confidence and areas with low one due to eyelashes and eyelids. Hence, these areas involve an imprecision in detecting iris and pupil. Such uncertainty can be further exploited for identification.