A Robust Iris Segmentation Scheme Based on Improved U-Net

Zhang, Wei; Lu, Xiaoqi; Gu, Yushen; Yang, Liu; Meng, Xianjing; Li, Jing

doi:10.1109/access.2019.2924464

Cited by 58 publications

(28 citation statements)

References 23 publications

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“…Its usage can be expanded further by considering different types of archaeo‐geophysical data and thus become a robust tool in archaeological prospection. On top of the above, U‐Net seems to be one of the most widely used architectures for image segmentation in various disciplines with a number of enhancements and upgrades of it arising continuously, benefiting its applicability (Ding, Yang, Wang, & Liu, ; Falk et al, ; Zhang et al, ).…”

Section: Discussionmentioning

confidence: 99%

Deep learning based automated analysis of archaeo‐geophysical images

Küçükdemirci

Sarris

2020

Archaeological Prospection

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Thanks to recent advances in deep learning (DL) and the increasing availability of large labeled/annotated datasets and trained network models, there has been impressive progress in the automated analysis of images from different scientific domains such as medicine, microbiology, astronomy and remote sensing. The automated analysis of archaeo-geophysical data is also considered important due to the large spatial extent of areas covered by landscape surveys using multi-sensor arrays driven by motorized carts and subsequently the large volume of collected data. In this work, a convolutional neural network (CNN) is built by Python 3.6 programming language using the Deep Learning Library of Keras with Tensorflow backends, a library that implements the building blocks for CNN. The network is trained from scratch adopting U-Net architecture to accomplish an automatic analysis of the archaeo-geophysical features with emphasis on ground-penetrating radar (GPR) anomalies. K E Y W O R D S archaeo-geophysics, convolutional neural networks (CNNs), deep learning, feature extraction, GPR (ground-penetrating radar), U-Net

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Section: Discussionmentioning

confidence: 99%

Deep learning based automated analysis of archaeo‐geophysical images

Küçükdemirci

Sarris

2020

Archaeological Prospection

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“…Thereafter, scholars have implemented pre-designed (off-the-shelf, existing) [ 28 , 29 , 30 , 31 ] or custom-built [ 1 , 32 , 33 ] fully connected networks (FCN) models for iris segmentation and achieved good segmentation accuracy on various iris databases. In Lian et al [ 31 ], Lozej et al [ 34 ], Wu and Zhao [ 35 ], and Zhang et al [ 36 ], scholars employed alternatives of U-Net [ 37 ] for iris segmentation. Despite the success of U-Net, these schemes still have some shortcomings [ 38 ], as described below: (1) The skip connection unreasonably forces the aggregation of the same scale feature maps of the encoder and decoder; (2) the optimal depth of the model is not yet known, so immense architecture searches are required, which sometimes leads to invalid collections of models with different depths.…”

Section: Related Workmentioning

confidence: 99%

Robust Iris Segmentation Algorithm in Non-Cooperative Environments Using Interleaved Residual U-Net

Putri

Aslam

et al. 2021

Sensors

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Iris segmentation plays an important and significant role in the iris recognition system. The prerequisite for accurate iris recognition is the correctness of iris segmentation. However, the efficiency and robustness of traditional iris segmentation methods are severely challenged in a non-cooperative environment because of unfavorable factors, for instance, occlusion, blur, low resolution, off-axis, motion, and specular reflections. All of the above factors seriously reduce the accuracy of iris segmentation. In this paper, we present a novel iris segmentation algorithm that localizes the outer and inner boundaries of the iris image. We propose a neural network model called “Interleaved Residual U-Net” (IRUNet) for semantic segmentation and iris mask synthesis. The K-means clustering is applied to select saliency points set in order to recover the outer boundary of the iris, whereas the inner border is recovered by selecting another set of saliency points on the inner side of the mask. Experimental results demonstrate that the proposed iris segmentation algorithm can achieve the mean IOU value of 98.9% and 97.7% for inner and outer boundary estimation, respectively, which outperforms the existing approaches on the challenging CASIA-Iris-Thousand database.

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“…Despite that, there were limitations such as occlusion due to hair, eyelashes, and eyeglasses, robust reflection, poor illumination, and blurring [175]. As such, Zhang et al [183] proposed four network schemes that combined the dilated convolution method [184] and the U-Net [181]. e dilated convolution method can extract more information about the iris image and enhance both performance and the efficiency of the iris segmentation method.…”

Section: U-netmentioning

confidence: 99%

Iris Recognition Development Techniques: A Comprehensive Review

2021

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Recently, iris recognition techniques have achieved great performance in identification. Among authentication techniques, iris recognition systems have received attention very much due to their rich iris texture which gives robust standards for identifying individuals. Notwithstanding this, there are several challenges in unrestricted recognition environments. In this article, the researchers present the techniques used in different phases of the recognition system of the iris image. The researchers also reviewed the methods associated with each phase. The recognition system is divided into seven phases, namely, the acquisition phase in which the iris images are acquired, the preprocessing phase in which the quality of the iris image is improved, the segmentation phase in which the iris region is separated from the background of the image, the normalization phase in which the segmented iris region is shaped into a rectangle, the feature extraction phase in which the features of the iris region are extracted, the feature selection phase in which the unique features of the iris are selected using feature selection techniques, and finally the classification phase in which the iris images are classified. This article also explains the two approaches of iris recognition which are the traditional approach and the deep learning approach. In addition, the researchers discuss the advantages and disadvantages of previous techniques as well as the limitations and benefits of both the traditional and deep learning approaches of iris recognition. This study can be considered as an initial step towards a large-scale study about iris recognition.

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A Robust Iris Segmentation Scheme Based on Improved U-Net

Cited by 58 publications

References 23 publications

Deep learning based automated analysis of archaeo‐geophysical images

Deep learning based automated analysis of archaeo‐geophysical images

Robust Iris Segmentation Algorithm in Non-Cooperative Environments Using Interleaved Residual U-Net

Iris Recognition Development Techniques: A Comprehensive Review

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