This paper advances the state of the art in human examination of iris images by (1) assessing the impact of different iris conditions in identity verification, and (2) introducing an annotation step that improves the accuracy of people's decisions. In a first experimental session, 114 subjects were asked to decide if pairs of iris images depict the same eye (genuine pairs) or two distinct eyes (impostor pairs). The image pairs sampled six conditions: (1) easy for algorithms to classify, (2) difficult for algorithms to classify, (3) large difference in pupil dilation, (4) disease-affected eyes, (5) identical twins, and (6) post-mortem samples. In a second session, 85 of the 114 subjects were asked to annotate matching and non-matching regions that supported their decisions. Subjects were allowed to change their initial classification as a result of the annotation process. Results suggest that: (a) people improve their identity verification accuracy when asked to annotate matching and non-matching regions between the pair of images, (b) images depicting the same eye with large difference in pupil dilation were the most challenging to subjects, but benefited well from the annotation-driven classification, (c) humans performed better than iris recognition algorithms when verifying genuine pairs of post-mortem and disease-affected eyes (i.e., samples showing deformations that go beyond the distortions of a healthy iris due to pupil dilation), and (d) annotation does not improve accuracy of analyzing images from identical twins, which remain confusing for people.
Bowyer al @cite_11 investigated people's ability to recognize right and left irises as belonging to the same person or not. Through experiments, they discovered that humans perceive texture similarities that are not detected by automated solutions. As a consequence, they can correctly guess, with only three seconds viewing, if left and right eyes belong to the same individual. When evaluating near-infrared images of the whole eye, subjects achieved an accuracy of 86 evaluating images with iris portions masked out, subjects achieved an accuracy of 83 ' ratings of image pairs were collected using a five-level scale, ranging from (1) same individual (certain)'', (2) same individual (likely)'', (3) uncertain'', (4) different people (likely)'', to (5) different people (certain)''.
@cite_11
2103393227
The iris codes for the left and right iris of a person have previously been reported to be uncorrelated. We replicate this result using images from 327 persons from the iris image dataset used in the Iris Challenge Evaluations. The same images are then used in an experiment in which subjects view a left and a right iris image, and judge whether they are correctly paired by similarity of iris texture. Subjects are able to distinguish between iris images belonging to the same person versus belonging to different persons with over 86 accuracy overall, and over 93 accuracy when they judge their decision as confident. Thus, there clearly is a similarity in the texture pattern of the left and right iris of the same person that can reliably be detected by novice observers with a brief viewing time. This is the first research that we are aware of that investigates the abilities of human observers in judging similarity or dissimilarity of iris texture.