Yes / Multimodal biometric systems have been widely
applied in many real-world applications due to its ability to
deal with a number of significant limitations of unimodal
biometric systems, including sensitivity to noise, population
coverage, intra-class variability, non-universality, and
vulnerability to spoofing. In this paper, an efficient and
real-time multimodal biometric system is proposed based
on building deep learning representations for images of
both the right and left irises of a person, and fusing the
results obtained using a ranking-level fusion method. The
trained deep learning system proposed is called IrisConvNet
whose architecture is based on a combination of Convolutional
Neural Network (CNN) and Softmax classifier to
extract discriminative features from the input image without
any domain knowledge where the input image represents
the localized iris region and then classify it into one of N
classes. In this work, a discriminative CNN training scheme
based on a combination of back-propagation algorithm and
mini-batch AdaGrad optimization method is proposed for
weights updating and learning rate adaptation, respectively.
In addition, other training strategies (e.g., dropout method,
data augmentation) are also proposed in order to evaluate
different CNN architectures. The performance of the proposed
system is tested on three public datasets collected
under different conditions: SDUMLA-HMT, CASIA-Iris-
V3 Interval and IITD iris databases. The results obtained
from the proposed system outperform other state-of-the-art
of approaches (e.g., Wavelet transform, Scattering transform,
Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases
and a recognition time less than one second per person.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/15682 |
| Date | 24 October 2017 |
| Creators | Al-Waisy, Alaa S., Qahwaji, Rami S.R., Ipson, Stanley S., Al-Fahdawi, Shumoos, Nagem, Tarek A.M. |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Published version |
| Rights | © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made., CC-BY |
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