Iris segmentation method based on improved UNet++

Huo, Guang; Lin, Dawei; Yuan, Meng

doi:10.1007/s11042-022-13198-z

Cited by 14 publications

(11 citation statements)

References 39 publications

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“…29 Wang et al 30 put forwarded a multi-task deep learning iris segmentation method, IrisParseNet, based on UNet. Huo et al 31 proposed a method that employs a UNet++ network fusion attention mechanism (AM-UNet++) to segment the iris. Chen et al 32 proposed a deep learning-based double attention dense connection network (DADCNet) to segment the iris.…”

Section: Methods For Iris Segmentationmentioning

confidence: 99%

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Robust iris segmentation based on ConvNeXts

et al. 2023

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At present, most iris segmentation methods based on deep learning have poor robustness in the face of iris images collected in non-cooperative environments (with partial occlusion, distortion, etc.), and their performance decreases to varying degrees. Inspired by the visual transformer (ViT), we combined the advantages of the ViT and ConvNeXts networks to propose a deep learning-based robust iris segmentation method called CINet. Specifically, we introduced global region aware (GRA) in ConvNeXts to capture global spatial information. It increases the sensitivity of the model to the inner and outer boundaries of the iris, achieving efficient iris segmentation. In addition, it also suppresses noise information irrelevant to the iris region, thus improving the robustness of the model. We used global channel normalization instead of batch normalization, which suppresses some unimportant channel information, further enhancing the network's performance. Experimental results demonstrate that GRA provides important feature information, which is essential for efficient iris segmentation. We verify the effectiveness of the proposed method on three benchmark iris datasets.

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Section: Methods For Iris Segmentationmentioning

confidence: 99%

“…put forwarded a multi-task deep learning iris segmentation method, IrisParseNet, based on UNet. Huo et al 31 . proposed a method that employs a UNet++ network fusion attention mechanism (AM-UNet++) to segment the iris.…”

Section: Related Workmentioning

confidence: 99%

Robust iris segmentation based on ConvNeXts

et al. 2023

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“…In addition to a single U-Net, [100,101] combines the advantages of other networks to perform iris segmentation tasks. It can make the network conveniently process complicated iris network.…”

Section: Segmentation Based On U-shaped Neural Networkmentioning

confidence: 99%

“…Experimental results show that the proposed model can improve accuracy and reduce error rates. In [101], an end-to-end encoder-decoder model based on an improved UNet++ is proposed for iris segmentation, referred to as the attention mechanism UNet++. First, the researchers chose EfficientNetV2 [102] as the backbone in [101] to improve the training speed and reduce the number of network parameters.…”

Section: Segmentation Based On U-shaped Neural Networkmentioning

confidence: 99%

“…In [101], an end-to-end encoder-decoder model based on an improved UNet++ is proposed for iris segmentation, referred to as the attention mechanism UNet++. First, the researchers chose EfficientNetV2 [102] as the backbone in [101] to improve the training speed and reduce the number of network parameters. Secondly, to make full use of the captured location information, enhance the semantic information on the channels to suppress extraneous noise interference, and enhance the discriminability learning of iris regions to improve the recognisability of iris features, the researchers embedded an attention module in the downsampling process of UNet++.…”

Section: Segmentation Based On U-shaped Neural Networkmentioning

confidence: 99%

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Deep Learning for Iris Recognition: A Review

Yin¹,

He²,

Zhang³

et al. 2023

Preprint

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Iris recognition is a secure biometric technology known for its stability and privacy. With no two irises being identical and little change throughout a person's lifetime, iris recognition is considered more reliable and less susceptible to external factors than other biometric recognition methods. Unlike traditional machine learning-based iris recognition methods, deep learning technology does not rely on feature engineering and boasts excellent performance. This paper collects 120 relevant papers to summarize the development of iris recognition based on deep learning. We first introduce the background of iris recognition and the motivation and contribution of this survey. Then, we present the common datasets widely used in iris recognition. After that, we summarize the key tasks involved in the process of iris recognition based on deep learning technology, including identification, segmentation, presentation attack detection, and localization. Finally, we discuss the challenges and potential development of iris recognition. This review provides a comprehensive sight of the research of iris recognition based on deep learning.

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