In the last few years, face morphing [1], [2] attacks has been shown to be a complex challenge for Face Recognition Systems (FRSs). Thus, the evaluation of other biometric modalities such as fingerprint, iris, and others must be explored and evaluated to enhance biometric systems. This work proposes an end-to-end framework to produce iris morphs at the image level, creating morphs from periocular iris images. This framework considers different stages such as iris pair selection from different subjects, segmentation, morph creation, and a new iris recognition system. In order to create realistic morphed images, two approaches for subject selection are proposed: random selection and similar pupil radius size selection. A vulnerability analysis and a Single Morphing Attack Detection algorithm were also explored. The results show that this approach obtained very realistic images that can confuse conventional iris recognition systems.

This paper proposes a new method to estimate behavioural curves from Near-Infra-Red (NIR) iris images for classifying Fitness for Duty using a biometric capture device. Fitness for Duty (FFD) techniques detect whether a subject is Fit to safely perform a given task, which means no reduced alertness condition and security, or the subject is unfit, that could impact a reduced alertness condition by sleepiness or consumption of alcohol and drugs. The analysis showed essential differences in pupil and iris behaviour to classify the workers in “Fit” or “Unfit” conditions. The best results can distinguish subjects robustly under alcohol, drug consumption, and sleep conditions. The Multi-Layer-Perceptron and Gradient Boosted Machine reached the best results in all groups with an overall accuracy for Fit and Unfit classes of 74.0% and 75.5%, respectively. These results open a new application for iris capture devices.

Currently, it is ever more common to access online services for activities which formerly required physical attendance. From banking operations to visa applications, a significant number of processes have been digitised, especially since the advent of the COVID-19 pandemic, requiring remote biometric authentication of the user. On the downside, some subjects intend to interfere with the normal operation of remote systems for personal profit by using fake identity documents, such as passports and ID cards. Deep learning solutions to detect such frauds have been presented in the literature. However, due to privacy concerns and the sensitive nature of personal identity documents, developing a dataset with the necessary number of examples for training deep neural networks is challenging. This work explores three methods for synthetically generating ID card images to increase the amount of data while training fraud-detection networks. These methods include computer vision algorithms and Generative Adversarial Networks. Our results indicate that databases can be supplemented with synthetic images without any loss in performance for the print/scan Presentation Attack Instrument Species (PAIS) and a loss in performance of 1% for the screen capture PAIS.

The high demand for ever more accurate biometric systems has driven the search for methods that reconstruct the iris surface in a 3D model. The intent in adding the depth dimension is to improve accuracy even in large databases. Here, we present a novel approach to iris recognition from 3D models. First, the iris 3D model is reconstructed from a single image using irisDepth, a CNN based method. Then, a 3D descriptor called Spin Image is obtained for keypoints of the 3D model. After that, matches are found between keypoints in the query and the reference 3D models using k-dimensional trees. Finally, those keypoint matches are used to determine the spatial transformation that best aligns the 3D models. A combination of the transformation error and the inlier ratio is used as the metric to assess the similarity of two iris 3D models. We applied this method in a dataset of 100 eyes and 2,000 iris 3D models. Our results indicate that using the proposed method is more effective than alternative methods, such as Dougman's iris code, point-to-point distance between the 3D models, the 3D rubber sheet model, and CNN-based methods.

In most iris recognition systems the texture of the iris image is either the result of the interaction between the iris and Near Infrared (NIR) light, or between the iris pigmentation and visible-light. The iris, however, is a three-dimensional organ, and the information contained on its relief is not being exploited completely. In this article, we present an image acquisition method that enhances viewing the structural information of the iris. Our method consists of adding lateral illumination to the visible light frontal illumination to capture the structural information of the muscle fibers of the iris on the resulting image. These resulting images contain highly textured patterns of the iris. To test our method, we collected a database of 1,920 iris images using both a conventional NIR device, and a custom-made device that illuminates the eye in lateral and frontal angles with visible-light (LFVL). Then, we compared the iris recognition performance of both devices by means of a Hamming distance distribution analysis among the corresponding binary iris codes. The ROC curves show that our method produced more separable distributions than those of the NIR device, and much better distribution than using frontal visible-light alone. Eliminating errors produced by images captured with different iris dilation (13 cases), the NIR produced inter-class and intra-class distributions that are completely separable as in the case of LFVL. This acquisition method could also be useful for 3D iris scanning.

The need to verify identity has become an everyday experience for most people. Biometrics is the principal means for reliable identification of people. Although iris recognition is the most reliable current technique for biometric identification, it has limitations because only segments of the iris are available due to occlusions from the eyelids, eyelashes, specular highlights, etc. The goal of this research is to study iris reconstruction from several 2D near infrared iris images, adding depth information to iris recognition. We expect that adding depth information from the iris surface will make it possible to identify people from a smaller segment of the iris. We designed a sensor for 2D near-infrared iris image acquisition. The method follows a pre-processing stage with the goal of performing iris enhancement, eliminating occlusions, reflections and extreme gray-level values, ending in iris texture equalization. The last step is the 3D iris model reconstruction based on several 2D iris images acquired at different angles. Results from each stage are presented.