Year-end Sale % OFF 
Abstract
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.
Highlights
The human iris is composed of two muscle systems and a sphincter to control the amount of light entering the retina [1]
The main contribution of this paper is to propose a new method to obtain a 3D model of the iris from a single image using Convolutional Neural Network (CNN)
RELATED METHODS IN DEPTH ESTIMATION USING CONVOLUTIONAL NEURAL NETWORKS Depth estimation by a CNN can be formulated as a regression problem, in which the input is an image, and the target is the depth value of every pixel, known as the depthmap
Summary
The human iris is composed of two muscle systems and a sphincter to control the amount of light entering the retina [1]. These muscular fibers, as well as the pigmentation, provide a unique texture to each iris that can be used for identification [2]. In recent years, a 3D iris scanning method that exploits the 3D relief of the iris has been proposed [9]–[11] This method reconstructs a 3D model of the iris surface using images from several perspectives and Structure from Motion (SfM) algorithms [12], [13]. The 3D iris model opens new frontiers for biometric applications, as well as in
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
