Gradual Iris Code Construction from Close-Up Eye Video

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.