DADCNet: Dual attention densely connected network for more accurate real iris region segmentation

Chen, Ying; Gan, Huimin; Zeng, Zhuang; Chen, Huiling

doi:10.1002/int.22649

Cited by 21 publications

(15 citation statements)

References 40 publications

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“…It can be seen from Table 8 that for the dataset MICHE-I, our proposed method achieved the highest Nice1 value of 0.66, which was equal to IrisParseNet [ 15 ]. Our F1 value was 0.02% higher than the previous best method, DADCNet [ 34 ]. Both IrisParseNet [ 15 ] and DADCNet [ 34 ] use the same data augmentation strategy, which expands the training set by a factor of five, whereas our proposed method did not use data augmentation and still alleviated the overfitting problem, validating the superiority of our method.…”

Section: Methodsmentioning

confidence: 58%

“…To further verify how advanced and encouraging the proposed method was, we compared it with a large number of state-of-the-art methods, which are divided into two main categories, one being non-deep-learning traditional methods [ 7 , 11 , 27 , 32 , 64 , 65 , 66 , 67 , 68 ], and the other being CNN-based deep learning methods [ 15 , 17 , 19 , 20 , 34 , 35 , 69 , 70 , 71 , 72 ]. It can be observed from Table 6 , Table 7 , Table 8 , Table 9 and Table 10 that the deep learning methods outperformed the traditional methods, and our proposed method was the optimal method among the deep learning methods.…”

Section: Methodsmentioning

confidence: 99%

“…Wang et al [ 15 ] propose an elaborate partitioned network with attention modules to obtain masked and parameterized inner and outer boundaries by optimizing a unified multitasking network. The double attention densely connected network (DADCNET) proposed by Chen et al [ 34 ] contains two attention modules and improves skip connections that replace the corresponding GT images using mask images segmented by deep learning methods. Wang et al [ 35 ] propose a lightweight fully connected neural network, using a weighted loss, multi-level-feature dense-fusion module, with multi-supervised training of a multi-scale image, and a generative adversarial network to improve mobile iris segmentation performance.…”

Section: Related Workmentioning

confidence: 99%

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Supervised Contrastive Learning and Intra-Dataset Adversarial Adaptation for Iris Segmentation

Zhou

Liu

Zhu

et al. 2022

Entropy

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Precise iris segmentation is a very important part of accurate iris recognition. Traditional iris segmentation methods require complex prior knowledge and pre- and post-processing and have limited accuracy under non-ideal conditions. Deep learning approaches outperform traditional methods. However, the limitation of a small number of labeled datasets degrades their performance drastically because of the difficulty in collecting and labeling irises. Furthermore, previous approaches ignore the large distribution gap within the non-ideal iris dataset due to illumination, motion blur, squinting eyes, etc. To address these issues, we propose a three-stage training strategy. Firstly, supervised contrastive pretraining is proposed to increase intra-class compactness and inter-class separability to obtain a good pixel classifier under a limited amount of data. Secondly, the entire network is fine-tuned using cross-entropy loss. Thirdly, an intra-dataset adversarial adaptation is proposed, which reduces the intra-dataset gap in the non-ideal situation by aligning the distribution of the hard and easy samples at the pixel class level. Our experiments show that our method improved the segmentation performance and achieved the following encouraging results: 0.44%, 1.03%, 0.66 %, 0.41%, and 0.37% in the Nice1 and 96.66%, 98.72%, 93.21%, 94.28%, and 97.41% in the F1 for UBIRIS.V2, IITD, MICHE-I, CASIA-D, and CASIA-T.

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Section: Methodsmentioning

confidence: 58%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Supervised Contrastive Learning and Intra-Dataset Adversarial Adaptation for Iris Segmentation

Zhou

Liu

Zhu

et al. 2022

Entropy

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“…In the above formula,   The pixels of the test image block and the answer sheet image are superimposed and calculated, and the result R is the correlation coefficient, whose value range is   0,1 . The higher the value of R is, the more similar the pixels of this part of the answer sheet image are to the pixels of the test image block [9]. After all the four parts are scanned, the maximum R value of each part is marked, so the coordinate positions of the four corners are determined.…”

Section: T X Y I X X Y Y R X Y T X Y I X X Y Ymentioning

confidence: 99%

Intelligent segmentation of subjective question area by removing template in intelligent marking

Xin

Zhang²

2023

Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022)

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With the rapid development of computer technology and communication technology, computers have been applied to all areas of people's lives. In the field of education, it is a necessary function for the intellectualization of the education system to realize the automatic marking of examination papers by computer. In intelligent marking, the traditional method is used to segment the subjective question area of the test paper, which can not effectively segment different types of answer sheets and has the defects of low segmentation accuracy. The edge detection method is used to correct the subjective question area of the answer sheet, and the boundary box of the subjective question part is accurately screened by positioning the top, left, and bottom positioning lines of the answer sheet to complete the segmentation of the subjective question area in marking. The experimental results show that the correct rate of the new method is more than 95%, which is much higher than that of the traditional method. The new method can be widely used in intelligent marking work, and can effectively improve the efficiency of intelligent marking.

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“…Lu et al 34 proposed a contour-based model that combines the coarse and fine localization results for off-angle and occluded iris segmentation and recognition. DadcNet 35 employed two attention modules and an improved skip connection to segment the real iris region more accurately than the corresponding ground-truth image. Then, iris segmentation networks that adopted self-designed encoderdecoder architectures [14][15][16][17] have obtained remarkable segmentation accuracy on different iris databases.…”

Section: Iris Segmentation and Localizationmentioning

confidence: 99%

LiSeNet: multitask lightweight segmentation network for accurate and complete iris segmentation

Sun

Liu

et al. 2022

J. Electron. Imag.

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. The demand for applying the iris segmentation model on mobile devices has been growing rapidly. Most current segmentation networks have an enormous amount of parameters, hence unsuitable for mobile devices, while other small memory footprint models follow the spirit of classification networks and ignore the inherent characteristic of segmentation. To address the challenge, we propose a lightweight segmentation network (LiSeNet) for iris segmentation of noisy images. Unlike previous studies that only focus on improving the accuracy of segmentation masks, LiSeNet can simultaneously obtain segmentation masks, parameterized pupillary and limbic boundaries of the iris, further enabling CNN-based iris segmentation to be applied in any regular iris recognition systems. We first propose a multiscale concatenate (MSC) Block, which connects multiple sizes of convolution kernels in a dense manner, gradually reduces the dimension of feature maps and uses the aggregation of them for image representation. Based on the MSC block, we develop a two-stage refinement encoder to aggregate discriminative features through subnetwork feature reuse and substage feature reassess, thus obtaining a sufficient receptive field and enhancing the model learning ability. To exploit object contextual information more efficiently, we further devise a grouped spatial attention to emphasize the important features and suppress irrelevant noises through a gating mechanism in the decoder. Extensive experiments on three challenging iris datasets show that LiSeNet, without any complicated postprocessing, achieves competitive or state-of-the-art performance with only 2.2M parameters, being 14 × smaller than the previous best method. Code will be publicly available.

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DADCNet: Dual attention densely connected network for more accurate real iris region segmentation

Cited by 21 publications

References 40 publications

Supervised Contrastive Learning and Intra-Dataset Adversarial Adaptation for Iris Segmentation

Supervised Contrastive Learning and Intra-Dataset Adversarial Adaptation for Iris Segmentation

Intelligent segmentation of subjective question area by removing template in intelligent marking

LiSeNet: multitask lightweight segmentation network for accurate and complete iris segmentation

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