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.
There are only a few works related to human examination of iris images. Stark al @cite_24 studied how people classify iris textures into categories. They used a software tool that allowed subjects to browse a set of segmented near-infrared iris images and use a drag-and-drop scheme to organize the images into groups based on their perception of the iris textures. They found that people consistently identify similar categories and subcategories of irises.
@cite_24
2075621163 2525871848 2138087096 2103393227
We report on an experiment in which observers were asked to browse a set of 100 iris images and group them into categories based on similarity of overall texture appearance. Results indicate that there is a natural categorization of iris images into a small number of high-level categories, and then also into subcategories. Also, the categorization reflects the Caucasian Asian ethnicity of the person. This iris texture categorization has potential application in, for example, creating an indexing algorithm to speed search of an iris database and or determining soft biometric traits of a person. Iris pattern is commonly regarded as a kind of phenotypic feature without relation to the genes. In this paper, we propose a novel ethnic classification method based on the global texture information of iris images. So we would argue that iris texture is race related, and its genetic information is illustrated in coarse scale texture features, rather than preserved in the minute local features of state-of-the-art iris recognition algorithms. In our scheme, a bank of multichannel 2D Gabor filters is used to capture the global texture information and AdaBoost is used to learn a discriminant classification principle from the pool of the candidate feature set. Finally iris images are grouped into two race categories, Asian and non-Asian. Based on the proposed method, we get an encouraging correct classification rate (CCR) of 85.95 on a mixed database containing 3982 iris samples in our experiments. Iris pattern is commonly regarded as a kind of phenotypic feature without relation to genes. In our previous work, we argued that iris texture is race related, and its genetic information is illustrated in coarse scale texture features, rather than preserved in the minute local features of state-of-the-art iris recognition algorithms. In this paper, we propose a novel ethnic classification method based on learning appearance primitives of iris images. So we not only confirm that iris texture is race related, but also try to find out which kinds of iris visual primitives make iris images look different between Asian and non-Asian. In our scheme, we learned a small finite vocabulary of micro-structures, which are called iris-textons, to represent visual primitives of iris images. Then we use iris-texton histogram to capture the difference between iris textures. Finally iris images are grouped into two race categories, Asian and non-Asian, by support vector machine (SVM). Based on the proposed method, we get a higher correct classification rate (CCR) of 91.02 than our previous method on a database containing 2400 iris samples. 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.