Quality-driven and real-time iris recognition from close-up eye videos

This paper deals with the computation of robust iris templates from video sequences. The main contribution is to propose (i) optimal tracking and robust detection of the pupil, (ii) smart selection of iris images to be enrolled, and (iii) multi-thread and quality-driven decomposition of tasks to reach real-time processing. The evaluation of the system was done on the Multiple Biometric Grand Challenge dataset. Especially we conducted a systematic study regarding the fragile bit rate and the number of merged images, using classical criteria. We reached an equal error rate value of 0.2% which reflects high performance on this database with respect to previous studies.