A 3D Iris Scanner From a Single Image Using Convolutional Neural Networks
Published in IEEE Access, 2020
A 3D model of the human iris adds an extra degree of freedom to iris recognition, helping identify people in larger databases even when only a portion of the iris is available. This work develops a 3D iris scanner that reconstructs a 3D iris model from a single 2D image using a depth-estimation Convolutional Neural Network. To train and evaluate the method, we built a dataset of 26,520 real iris images from 120 subjects and a synthetic dataset of 72,000 iris images with aligned depthmaps. The proposed approach was validated against step-pyramid 3D-printed models, synthetic test images, and OCT scans of both eyes of a subject. A 3D rubber sheet generated from the reconstructed iris model improved iris recognition performance by 48% over the standard 2D iris code. Additional contributions include reducing the acquisition and processing time and simplifying the image acquisition system.
How to cite
@article{benalcazar20203diris,
author = {Benalcazar, Daniel P. and Zambrano, Jorge E. and Bastias, Diego and Perez, Claudio A. and Bowyer, Kevin W.},
journal = {IEEE Access},
title = {A {3D} Iris Scanner From a Single Image Using Convolutional Neural Networks},
year = {2020},
volume = {8},
pages = {98584--98599},
doi = {10.1109/ACCESS.2020.2996563}
}
Recommended citation: D. P. Benalcazar, J. E. Zambrano, D. Bastias, C. A. Perez and K. W. Bowyer, "A 3D Iris Scanner From a Single Image Using Convolutional Neural Networks," in IEEE Access, vol. 8, pp. 98584-98599, 2020, doi: 10.1109/ACCESS.2020.2996563.
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