High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Face Detection
International Journal of Computer Vision
Iris Recognition Algorithm Using Modified Log-Gabor Filters
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
ENCARA2: Real-time detection of multiple faces at different resolutions in video streams
Journal of Visual Communication and Image Representation
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
Improvements in video-based automated system for iris recognition (VASIR)
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Iris recognition by fusing different representations of multi-scale Taylor expansion
Computer Vision and Image Understanding
Local quality method for the iris image pattern
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Gradual iris code construction from close-up eye video
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
Sensitivity analysis for biometric systems: A methodology based on orthogonal experiment designs
Computer Vision and Image Understanding
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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. The selection method's 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 feasible. Furthermore, our study showed that in practice an automated best image selection is nearly equivalent to human selection.