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This paper advances the state of the art in human examination of iris imagesby (1) assessing the impact of different iris conditions in identityverification, and (2) introducing an annotation step that improves the accuracyof people's decisions. In a first experimental session, 114 subjects were askedto decide if pairs of iris images depict the same eye (genuine pairs) or twodistinct 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) identicaltwins, and (6) post-mortem samples. In a second session, 85 of the 114 subjectswere asked to annotate matching and non-matching regions that supported theirdecisions. Subjects were allowed to change their initial classification as aresult of the annotation process. Results suggest that: (a) people improvetheir identity verification accuracy when asked to annotate matching andnon-matching regions between the pair of images, (b) images depicting the sameeye with large difference in pupil dilation were the most challenging tosubjects, but benefited well from the annotation-driven classification, (c)humans performed better than iris recognition algorithms when verifying genuinepairs of post-mortem and disease-affected eyes (i.e., samples showingdeformations that go beyond the distortions of a healthy iris due to pupildilation), and (d) annotation does not improve accuracy of analyzing imagesfrom identical twins, which remain confusing for people.

Performance of Humans in Iris Recognition: The Impact of Iris Condition and Annotation-driven Verification