2 research outputs found
Iris Recognition with Image Segmentation Employing Retrained Off-the-Shelf Deep Neural Networks
This paper offers three new, open-source, deep learning-based iris
segmentation methods, and the methodology how to use irregular segmentation
masks in a conventional Gabor-wavelet-based iris recognition. To train and
validate the methods, we used a wide spectrum of iris images acquired by
different teams and different sensors and offered publicly, including data
taken from CASIA-Iris-Interval-v4, BioSec, ND-Iris-0405, UBIRIS,
Warsaw-BioBase-Post-Mortem-Iris v2.0 (post-mortem iris images), and
ND-TWINS-2009-2010 (iris images acquired from identical twins). This varied
training data should increase the generalization capabilities of the proposed
segmentation techniques. In database-disjoint training and testing, we show
that deep learning-based segmentation outperforms the conventional (OSIRIS)
segmentation in terms of Intersection over Union calculated between the
obtained results and manually annotated ground-truth. Interestingly, the
Gabor-based iris matching is not always better when deep learning-based
segmentation is used, and is on par with the method employing Daugman's based
segmentation.Comment: Paper submitted for the IEEE International Conference on Biometrics
(ICB2019
Post-mortem Iris Recognition with Deep-Learning-based Image Segmentation
This paper proposes the first known to us iris recognition methodology
designed specifically for post-mortem samples. We propose to use deep
learning-based iris segmentation models to extract highly irregular iris
texture areas in post-mortem iris images. We show how to use segmentation masks
predicted by neural networks in conventional, Gabor-based iris recognition
method, which employs circular approximations of the pupillary and limbic iris
boundaries. As a whole, this method allows for a significant improvement in
post-mortem iris recognition accuracy over the methods designed only for
ante-mortem irises, including the academic OSIRIS and commercial IriCore
implementations. The proposed method reaches the EER less than 1% for samples
collected up to 10 hours after death, when compared to 16.89% and 5.37% of EER
observed for OSIRIS and IriCore, respectively. For samples collected up to 369
hours post-mortem, the proposed method achieves the EER 21.45%, while 33.59%
and 25.38% are observed for OSIRIS and IriCore, respectively. Additionally, the
method is tested on a database of iris images collected from ophthalmology
clinic patients, for which it also offers an advantage over the two other
algorithms. This work is the first step towards post-mortem-specific iris
recognition, which increases the chances of identification of deceased subjects
in forensic investigations. The new database of post-mortem iris images
acquired from 42 subjects, as well as the deep learning-based segmentation
models are made available along with the paper, to ensure all the results
presented in this manuscript are reproducible.Comment: Paper submitted for the Elsevier Image and Vision Computing Journal
on Jan 5th, 2019, revised versio