Iris Segmentation Using Fully Convolutional Encoder–Decoder Networks

Jalilian, Ehsaneddin; Uhl, Andreas

doi:10.1007/978-3-319-61657-5_6

Cited by 63 publications

(40 citation statements)

References 32 publications

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“…In [55], [56], authors showed that deep learning-based methods are better for iris segmentation than traditional methods, even for cases with blurred or partial iris 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120 125 130 135 140 145 150 155 160 165 170 175 180 185 190 195 200 205 210 213 Subject number 0 20 40 60 80 Post-mortem interval (hours)…”

Section: Iris Recognitionmentioning

confidence: 99%

Post-Mortem Iris Recognition—A Survey and Assessment of the State of the Art

et al. 2020

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Post-mortem biometrics entails utilizing the biometric data of a deceased individual for determining or verifying human identity. Due to fundamental biological changes that occur in a person's biometric traits after death, post-mortem data can be significantly different from ante-mortem data, introducing new challenges for biometric sensors, feature extractors and matchers. This paper surveys research to date on the problem of using iris images acquired after death for automated human recognition. A comprehensive review of existing literature is complemented by a summary of the most recent results and observations offered in these publications. This survey is unique in several elements. Firstly, it is the first publication to consider iris recognition where gallery images are acquired before death (perimortem images) and the probe images are acquired after death from the same subjects. Secondly, results are presented from the largest database of peri-mortem and post-mortem iris images, collected from 213 subjects by two independent institutions located in the U.S. and Poland. Thirdly, post-mortem recognition viability is assessed using more than 20 iris recognition algorithms, ranging from the classic (e.g., Gabor filteringbased) to the modern (e.g., deep learning-based). Finally, we provide a medically informed commentary on post-mortem iris, analyze the reasons for recognition failures, and identify key directions for future research.

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Section: Iris Recognitionmentioning

confidence: 99%

Post-Mortem Iris Recognition—A Survey and Assessment of the State of the Art

et al. 2020

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“…Recently, CNNs have also been utilized for gender identification [24], iris segmentation [25], [26] and spoofperception [27], [28].…”

Section: Related Workmentioning

confidence: 99%

Noisy Iris Recognition Based on Deep Neural Network

Omran

Soliman

Salah

et al. 2020

Menoufia Journal of Electronic Engineering Research

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Iris recognition is one of the Biometric systems used for persons identification based on their special iris traits, which are unique featuresfor each individual. It is clear that the progress in deep learning show how efficient the extracted features from convolutional neural networks (CNNs) to describe the complex image patterns. However, the influence of noise is a serious problem in most image processing systems. It may ariseto the iris recognition systems due to environmental conditions that can affects the features extracted from the iris images. Hence, the objective of this paper is to study the performance of CNNs based Deep learning (Alex net, Vgg16 and Vgg19) when used for iris recognition with the presence of noise and compares it with Masek algorithm. Simulation results reveal that using the deep learning greatly improves iris recognition accuracy for Alex CNN. We achieve 100%, 100%, 88.9% for interval, lamp and twins datasets respectively.

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“…Hierarchical Convolutional Neural Network (HCNN) and Multi-scale Fully Convolution Network (MFCN) was proposed by Liu et al [14] to deal with noisy iris images acquired in long distance and in motion. Jalilian and Uhl [15] proposed three types of Fully Convolution Encoder-Decoder Networks (FCEDN) for iris segmentation. Yang et al [16] proposed a network model combining FCN with dilated convolution to segment iris, and trained and tested it on CASIA-iris-interval-v4.0, UBIRIS.v2 and IITD Delhi datasets with 98.6%, 98.4% and 95.7% accuracy, respectively.…”

Section: B Convolutional Neural Network (Cnn) For Iris Segmentationmentioning

confidence: 99%

A Robust Iris Segmentation Scheme Based on Improved U-Net

Zhang

et al. 2019

IEEE Access

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Iris segmentation plays an important role in the iris recognition system, and the accurate segmentation of iris can lay a good foundation for the follow-up work of iris recognition and can improve greatly the efficiency of iris recognition. We proposed four new feasible network schemes, and the best network model fully dilated convolution combining U-Net (FD-UNet) is obtained by training and testing on the same datasets. The FD-UNet uses dilated convolution instead of original convolution to extract more global features so that the details of images can be processed better. The proposed method is tested in the near-infrared illumination iris datasets (CASIA-iris-interval-v4.0 and ND-IRIS-0405) and the visible light illumination iris dataset (UBIRIS.v2). The f1 scores of our model on the CASIA-iris-interval-v4.0, ND-IRIS-0405, and UBIRIS.v2 datasets reached 97.36%, 96.74%, and 94.81%, respectively. The experimental results show that our network model improves the accuracy and reduces the error rate, which performs well on both near-infrared illumination and visible light illumination iris datasets with good robustness.

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Iris Segmentation Using Fully Convolutional Encoder–Decoder Networks

Cited by 63 publications

References 32 publications

Post-Mortem Iris Recognition—A Survey and Assessment of the State of the Art

Post-Mortem Iris Recognition—A Survey and Assessment of the State of the Art

Noisy Iris Recognition Based on Deep Neural Network

A Robust Iris Segmentation Scheme Based on Improved U-Net

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