We have successfully implemented a Video-based Automated System for Iris Recognition (VASIR), evaluating its successful performance on the MBGC dataset. The proposed method facilitates the ultimate goal of automatically detecting an eye area, extracting eye images, and selecting the best quality iris image from video frames. its performance is evaluated by comparing it to the selection performed by humans. Masek's algorithm was adapted to segment and normalize the iris region. Encoding the iris pattern and then completing the matching followed this stage. The iris templates from video images were compared to pre-existing still iris images for the purpose of the verification. This experiment has shown that even under varying illumination conditions, low quality, and off-angle video imagery, that iris recognition is nevertheless feasible. Furthermore, our study showed that in practice an automated best image selection is nearly equivalent to human selection.
Proceedings Title: ICB2009 (LNCS volume by Springer)
Conference Dates: June 2-5, 2009
Conference Location: Sassari, -1
Conference Title: 3rd IAPR / IEEE International Conference on Biometrics
Pub Type: Conferences
Biometrics, Iris Recognition, Eye Detection, Image Quality Measurement, VASIR, Iris Segmentation.