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 2161175920 2010903821 2107837559
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. As the standard iris biometric algorithm ''sees'' them, the left and right irises of the same person are as different as irises of unrelated people. Similarly, in terms of iris biometric matching, the eyes of identical twins are as different as irises of unrelated people. The left and right eyes of an individual or the eyes of identical twins are examples of genetically identical irises. In experiments with human observers viewing pairs of iris images acquired using an iris biometric system, we have found that there is recognizable similarity in the left and right irises of an individual and in the irises of identical twins. This result suggests that iris texture analysis different from that performed in the standard iris biometric algorithm may be able to answer questions that iris biometrics cannot answer. We are not aware of any previous systematic investigation of how well human examiners perform at identity verification using the same type of images as acquired for automated iris recognition. This paper presents results of an experiment in which examiners consider a pair of iris images to decide if they are either (a) two images of the same eye of the same person, or (b) images of two different eyes, with the two different individuals having the same gender, ethnicity and approximate age. Results suggest that novice examiners can readily achieve accuracy exceeding 90 and can exceed 96 when they judge their decision as “certain”. Results also suggest that examiners may be able to improve their accuracy with experience. Prior research has shown that the textural detail of the iris is sufficiently distinctive to distinguish identical twin siblings. However, no research has addressed the question of whether twins' irises are sufficiently similar in some sense to correctly determine that two irises are from twins. We conducted a human classification study in which participants were asked to label pairs of iris images as “twins” or “unrelated”. Participants were given three seconds to view each pair of images. We found that untrained humans can classify pairs of twins with more than 81 accuracy using the appearance of the iris alone, without any proximal image content such as eyelashes, eyelids, or tear duct visible. When expressing confident judgment, they are over 92 accurate.