In this paper, we present different pattern recognition approaches for automatically detecting tear ducts in iris acquired eye images for enhancing iris recognition and detecting mislabeling in datasets. Detecting the tear duct in an image will tell an iris recognition system whether the presented eye image is that of a left or a right eye. This will enable the iris matcher to match the enrolled image against images in the database belonging to the same side, thus reducing the error rates by eliminating the chance of matching a left iris to a right iris or vice-versa. This is a major problem in many single iris imaging acquisition devices currently deployed in the field where the data recorded is mislabeled due to human error. We present several techniques of detecting tear ducts, including boosted Haar features, support vector machines (SVM), and more traditional approaches like PCA and LDA. Finally, we show that tear duct detection improves the detection of left/right iris recognition over previous approaches.
['Ramzi Abiantun', 'Marios Savvides']
Tear-duct detector for identifying left versus right iris images