Multi‐source heterogeneous iris segmentation method based on lightweight convolutional neural network

Huo, Guang; Lin, Dawei; Meng, Yuan

doi:10.1049/ipr2.12621

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…Figure 11 is a graphical representation of the performance of several IS techniques. Our proposed MS-Net is compared with previous state-of-the-art methods that were based on the traditional approach [91] and CNN methods for IS [10], [15], [34], [39], [61], [92], [93], [94], [95], [96], [97], [98], [99], [100], [101], [102]. FM testing was conducted to compare the performance of MS-Net with earlier methods, utilising the same open database, the UBIRIS.v2.…”

Section: Comparison Of the Proposed Ms-net With Earlier State-of-the-...mentioning

confidence: 99%

MS-Net: Multi-Segmentation Network for the Iris Region Using Deep Learning in an Unconstrained Environment

et al. 2023

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Iris segmentation is a significant phase in the iris recognition process because segmentation errors cascade into all subsequent phases. Therefore, it is important that errors in iris segmentation are minimised. The U-Net architecture that uses a deep learning approach was previously adopted for this task, but its performance was affected by the deformation of iris images caused by various noise factors in unconstrained (non-ideal) environments. Scratches, blurriness, dirt, specular reflections and other noise factors are some of the challenges faced in unconstrained environments when eyeglasses are present in the original images. Additionally, the performance of the iris segmentation was degraded due to problems of exploding gradient or vanishing gradient and the loss of information. This paper proposes a multisegmentation network called MS-Net, based on a deep learning approach, that aims to capture high-level semantic features while maintaining spatial information to improve the accuracy of iris segmentation. MS-Net consists of three principal segments: a feature encoder network, a multi-scale context feature extractor network (MSCFE-Net) and a feature decoder network. MSCFE-Net a multi-scale context feature extractor network is constructed from a dilated residual multi-convolutional network module and a pyramid pooling residual model based on an attention convolutional module. In addition, the proposed MS-Net contains dense connections within the feature decoder network to decrease training difficulty, by using only a few training samples. The accuracy of MS-Net was evaluated on the CASIA-Iris.V4-1000 and UBIRIS.V2 databases. The performance of our proposed MS-Net method on the CASIA-Iris.V4-1000 and UBIRIS.V2 databases achieved an overall accuracy of 97.11% and 96.128%, respectively. Experiment results show that MS-Net is able to achieve better results compared to earlier methods used for the same purpose.

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Section: Comparison Of the Proposed Ms-net With Earlier State-of-the-...mentioning

confidence: 99%

MS-Net: Multi-Segmentation Network for the Iris Region Using Deep Learning in an Unconstrained Environment

et al. 2023

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“…In the current osteosarcoma image detection technology, the medical images available for diagnosing osteosarcoma include CT, X-ray and MRI images [12][13][14][15][16]. Among these detection options, MRI is not only free of radiation, biological damage, or ionizing radiation damage, but is more pronounced in soft tissue imaging [17,18].…”

Section: Introductionmentioning

confidence: 99%

Image segmentation technology based on transformer in medical decision‐making system

Gou

2023

IET Image Processing

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Due to the improvement in computing power and the development of computer technology, deep learning has pene‐trated into various fields of the medical industry. Segmenting lesion areas in medical scans can help clinicians make accurate diagnoses. In particular, convolutional neural networks (CNNs) are a dominant tool in computer vision tasks. They can accurately locate and classify lesion areas. However, due to their inherent inductive bias, CNNs may lack an understanding of long‐term dependencies in medical images, leading to less accurate grasping of details in the images. To address this problem, we explored a Transformer‐based solution and studied its feasibility in medical imaging tasks (OstT). First, we performed super‐resolution reconstruction on the original MRI image of osteosarcoma and improved the texture features of the tissue structure to reduce the error caused by the unclear tissue structure in the image during model training. Then, we propose a Transformer‐based method for medical image segmentation. A gated axial attention model is used, which augments existing architectures by introducing an additional control mechanism in the self‐attention module to improve segmentation accuracy. Experiments on real datasets show that our method outper‐forms existing models such as Unet. It can effectively assist doctors in imaging examinations.

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“…As one of the vital identity authentication features, iris recognition has numerous advantages, such as uniqueness, stability, collectability, and noninvasiveness, making it a hot topic in biometric recognition research. [1][2][3][4] Iris recognition is a complex process involving multiple steps, including iris image acquisition, preprocessing, iris segmentation, normalization, feature extraction, and iris recognition. 5 Among these, the conditions of iris image acquisition are very unstable and are affected by visible and infrared light factors, iris rotation, variable distance out of focus, eyelash occlusion, and other factors.…”

Section: Introductionmentioning

confidence: 99%

“…As one of the vital identity authentication features, iris recognition has numerous advantages, such as uniqueness, stability, collectability, and non-invasiveness, making it a hot topic in biometric recognition research 1 – 4 …”

Section: Introductionmentioning

confidence: 99%

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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Multi‐source heterogeneous iris segmentation method based on lightweight convolutional neural network

Cited by 6 publications

References 32 publications

MS-Net: Multi-Segmentation Network for the Iris Region Using Deep Learning in an Unconstrained Environment

MS-Net: Multi-Segmentation Network for the Iris Region Using Deep Learning in an Unconstrained Environment

Image segmentation technology based on transformer in medical decision‐making system

Robust iris segmentation based on ConvNeXts

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