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The development of new methods for biometrics using the 3-D surface of the iris could be useful in various applications, such as reliable identity verification of people, when only segments of the iris are available, the study of how the iris code changes with pupil dilation, and studying acute angle glaucoma and its relation to the 3-D iris structure. The goal of this paper was to build a 3-D model of the iris surface from several 2-D iris images, adding depth information to the iris model. We developed a 3-D iris scanner, which reconstructs a 3-D mesh model of the iris surface from several 2-D visible light images. First, a smartphone camera captures visible light iris images from different angles in a controlled illumination environment. Then, a structure-from-motion algorithm reconstructs a point-cloud 3-D model. Finally, the best-fitting 3-D mesh model is obtained using the screened Poisson surface reconstruction technique. Our results include the reconstruction of the 3-D iris models of ten subjects. These models contain an average of 11,000 3-D points. The spatial resolution of our device was measured as 11 µm by scanning a 3-D pattern of known dimensions. The 3-D model of the iris is compared with the results from an optical coherence tomography (OCT) performed on one iris. Our results show that our new 3-D iris scanning method produces a model with potential applications in biometrics and ophthalmology. INDEX TERMS 3D iris reconstruction, 3D iris scanner, biometrics, iris recognition, structure from motion.

This paper proposes a new framework to detect, segment, and estimate the localization of the eyes from a periocular Near-Infra-Red iris image under alcohol consumption. This stage will take part in the final solution to measure the fitness for duty. Fitness systems allow us to determine whether a person is physically or psychologically able to perform their tasks. Our segmentation framework is based on an object detector trained from scratch to detect both eyes from a single image. Then, two efficient networks were used for semantic segmentation; a Criss-Cross attention network and DenseNet10, with only 122,514 and 210,732 parameters, respectively. These networks can find the pupil, iris, and sclera. In the end, the binary output eye mask is used for pupil and iris diameter estimation with high precision. Five state-of-theart algorithms were used for this purpose. A mixed proposal reached the best results. A second contribution is establishing an alcohol behavior curve to detect the alcohol presence utilizing a stream of images captured from an iris instance. Also, a manually labeled database with more than 20k images was created. Our best method obtains a mean Intersection-over-Union of 94.54% with DenseNet10 with only 210,732 parameters and an error of only 1-pixel on average.INDEX TERMS Biometrics, Fitness for duty, segmentation, iris, alcohol.

A 3D model of the human iris provides an additional degree of freedom in iris recognition, which could help identify people in larger databases, even when only a piece of the iris is available. Previously, we reported developing a 3D iris scanner that uses 2D images of the iris from multiple perspectives to reconstruct a 3D model of the iris. This paper focuses on the development of a 3D iris scanner from a single image by means of a Convolutional Neural Network (CNN). The method is based on a depth-estimation CNN for the 3D iris model. A dataset of 26,520 real iris images from 120 subjects, and a dataset of 72,000 synthetic iris images with their aligned depthmaps were created. With these datasets, we trained and compared the depth estimation capabilities of available CNN architectures. We analyzed the performance of our method to estimate the iris depth in multiple ways: using real step pyramid printed 3D models, comparing the results to those of a test set of synthetic images, comparing the results to those of the OCT scans from both eyes of one subject, and generating the 3D rubber sheet from the 3D iris model proving the correspondence with the resulting 2D rubber sheet and binary codes. On a preliminary test the proposed 3D rubber sheet model increased iris recognition performance by 48% with respect to the standard 2D iris code. Other contributions include assessing the scanning resolution, reducing the acquisition and processing time to produce the 3D iris model, and reducing the complexity of the image acquisition system.INDEX TERMS 3D iris reconstruction, 3D iris scanner, biometrics, iris recognition, depth estimation.