SegDenseNet: Iris Segmentation for Pre-and-Post Cataract Surgery
Aditya Lakra,Pavani Tripathi,Rohit Keshari,Mayank Vatsa,Richa Singh +4 more
- pp 3150-3155
TLDR
In this paper, SegDenseNet, a deep learning algorithm based on DenseNet was proposed to handle cataract and post-cataract surgery cases in iris segmentation.Abstract:
Cataract is one of the major ophthalmic diseases worldwide which can potentially affect the performance of iris-based biometric systems. While existing research has shown that cataract does not have a major impact on iris recognition, our observations suggest that iris segmentation algorithms are not well equipped to handle cataract or post cataract surgery cases, thereby affecting the overall iris recognition performance. This paper presents an efficient iris segmentation algorithm with variations due to cataract and post cataract surgery. The proposed algorithm, termed as SegDenseNet, is a deep learning algorithm based on DenseNet. The experiments on the IIITD Cataract Surgery Database show that improving iris segmentation enhances the recognition performance by up to 25% across different sensors and matchers.read more
Citations
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Journal ArticleDOI
Deep neural network and data augmentation methodology for off-axis iris segmentation in wearable headsets.
TL;DR: In this article, a data augmentation methodology is presented and applied to generate a large dataset of off-axis iris regions and train a low-complexity deep neural network.
Journal ArticleDOI
PixISegNet: pixel-level iris segmentation network using convolutional encoder–decoder with stacked hourglass bottleneck
TL;DR: The authors’ model surpasses the performance of state-of-the-art Iris DenseNet framework by applying several strategies, including multi-scale/ multi-orientation training, model training from scratch, and proper hyper-parameterisation of crucial parameters.
Proceedings ArticleDOI
Fully Convolutional Networks and Generative Adversarial Networks Applied to Sclera Segmentation
TL;DR: In this paper, two approaches based on the Fully Convolutional Network (FCN) and on Generative Adversarial Network (GAN) are introduced for segmentation of the sclera.
Journal ArticleDOI
Iris Recognition Development Techniques: A Comprehensive Review
TL;DR: The researchers present the techniques used in different phases of the recognition system of the iris image and explains the two approaches of iris recognition which are the traditional approach and the deep learning approach.
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Unravelling Small Sample Size Problems in the Deep Learning World
TL;DR: A review of deep learning algorithms for small sample size problems in which the algorithms are segregated according to the space in which they operate, i.e. input space, model space, and feature space and a Dynamic Attention Pooling approach which focuses on extracting global information from the most discriminative sub-part of the feature map is presented.
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