Iris Identification in 3D

Cohen, Fernand S.; Sowmithran, Sowrirajan; Li, Chenxi

doi:10.1007/978-3-030-20205-7_27

Cited by 3 publications

(4 citation statements)

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“…A 3D model of the iris may open research lines in iris recognition and ophthalmology. In addition to increasing accuracy in iris recognition [11], obtaining 3D information of the iris could help in extreme pose detection [46]- [50]. Additionally, a 3D model of the iris could produce information similar to that of an OCT, which could help ophthalmologists in the detection of closure angle glaucoma [10], [14].…”

Section: Discussionmentioning

confidence: 99%

“…Traditionally, the texture of the iris has been analyzed using 2D images to produce accurate iris recognition [2]- [8]. However, 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].…”

Section: Introductionmentioning

confidence: 99%

“…The result is a 3D model that incorporates both depth and color information of the iris surface. The additional dimension aims to increase iris recognition accuracy particularly when the iris is occluded by eyelids, eyelashes, and reflections [9], [11]. The system recently developed by Cohen et al [11] tracks fiducial points from two or more near-infrared (NIR) images of the eye to create the 3D model.…”

Section: Introductionmentioning

confidence: 99%

“…The additional dimension aims to increase iris recognition accuracy particularly when the iris is occluded by eyelids, eyelashes, and reflections [9], [11]. The system recently developed by Cohen et al [11] tracks fiducial points from two or more near-infrared (NIR) images of the eye to create the 3D model. They then calculate the geometric error between two 3D models as the Mean Square Error (MSE) of candidate matching points.…”

Section: Introductionmentioning

confidence: 99%

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A 3D Iris Scanner From a Single Image Using Convolutional Neural Networks

et al. 2020

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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.

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