A 3D Iris Scanner From Multiple 2D Visible Light Images

Benalcazar, Daniel P.; Bastias, Diego; Perez, Claudio A.; Bowyer, Kevin W.

doi:10.1109/access.2019.2915786

Cited by 8 publications

(22 citation statements)

References 52 publications

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Derek Abbott . ophthalmology [10]. For example, the 3D iris model can potentially be used as a screening method for Closure Angle Glaucoma, a disease currently diagnosed with Optic Coherence Tomography (OCT) scans [9], [10], [14].…”

Section: Introductionmentioning

confidence: 99%

“…ophthalmology [10]. For example, the 3D iris model can potentially be used as a screening method for Closure Angle Glaucoma, a disease currently diagnosed with Optic Coherence Tomography (OCT) scans [9], [10], [14].…”

Section: Introductionmentioning

confidence: 99%

“…A method for reconstructing a 3D model of the iris surface from several images was introduced by Bastias et al [9] and improved by Benalcazar et al [10]. The improved method consists of the following steps: First, visible light (VL) images of the iris are captured from different perspectives.…”

Section: Introductionmentioning

confidence: 99%

“…These images are acquired with a custom device that illuminates the iris with Lateral and Frontal Visible Light (LFVL) [15]. Then, a modified SfM algorithm estimates the camera pose of every image jointly with a sparse 3D model of the iris [9], [10]. Then, a dense 3D point-cloud reconstruction is performed by extracting Shi-Tomasi keypoints from each image [10], [16].…”

Section: Introductionmentioning

confidence: 99%

“…Then, a modified SfM algorithm estimates the camera pose of every image jointly with a sparse 3D model of the iris [9], [10]. Then, a dense 3D point-cloud reconstruction is performed by extracting Shi-Tomasi keypoints from each image [10], [16]. Finally, the point-cloud model is converted into a mesh surface by the Screened Poisson Surface Reconstruction technique [17].…”

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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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Knowledge vector representation of three-dimensional convex polyhedrons and reconstruction of medical images using knowledge vector

Rani

Lakhwani

Kumar

2023

Multimed Tools Appl

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Three-dimensional image construction and reconstruction play an important role in various applications of the real world in the field of computer vision. In the last three decades, researchers are continually working in this area because construction and reconstruction is an important approach in medical imaging. Reconstruction of the 3D image allows us to find the lesion information of the patients which could offer a new and accurate approach for the diagnosis of the disease and it adds a clinical value. Considering this, we proposed novel approaches for the construction and reconstruction of the image. First, the novel construction algorithm is used to extract the features from an image using syntactic pattern recognition. The proposed algorithm is able to extract in-depth features in all possible directions and planes and also able to represent the 3D image into a textual form. These features vector is nothing but a string that consists of direction and length information in syntactic form. For the identification of syntactic grammar, a real 3D clay model was made and identified the different possible patterns in the image. According to the domain knowledge, in a 3D image, a pixel could be present in 26 possible directions and we incorporated all possible directions in the proposed algorithm. In the same way, for the reconstruction of the image novel algorithm is proposed. In this algorithm, the knowledge vector has been taken as an input and the algorithm is able to reconstruct a 3D image.Reconstruction allows us to explore the internal details of the 3D images such as the size, shape, and structure of the object which could take us one step ahead in the field of medical image processing. Performances of the proposed algorithms are evaluated on five medical image dataset and the datasets are collected from Pentagram research institute, Hyderabad and results are outperformed in real-time. The accuracy of the proposed method is 94.78% and the average execution time is 6.76 seconds which is better than state of art methods.

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