Iris Segmentation Using Feature Channel Optimization for Noisy Environments

In recent years, iris recognition has been widely used in various fields. As the first step of iris recognition, segmentation accuracy is of great significance to the final recognition. However, iris images exhibit a variety of noise in the real world, which leads to lower segmentation accuracy than...

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Bibliographic Details
Published inCognitive computation Vol. 12; no. 6; pp. 1205 - 1216
Main Authors Hao, Kangli, Feng, Guorui, Ren, Yanli, Zhang, Xinpeng
Format Journal Article
LanguageEnglish
Published New York Springer US 01.11.2020
Springer Nature B.V
Subjects
Accuracy
Algorithms
Artificial Intelligence
Biometrics
Computation by Abstract Devices
Computational Biology/Bioinformatics
Computer Science
Deep learning
Gradient flow
Image processing
Image segmentation
Methods
Optimization
Random noise
Semantics
Dilated convolution
Feature channel optimization
Dense block
U-Net
Iris segmentation
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Summary:In recent years, iris recognition has been widely used in various fields. As the first step of iris recognition, segmentation accuracy is of great significance to the final recognition. However, iris images exhibit a variety of noise in the real world, which leads to lower segmentation accuracy than the ideal case. To address this problem, this paper proposes an iris segmentation method using feature channel optimization for noisy images. The method for non-ideal environments with noise is more suitable for practical applications. We add dense blocks and dilated convolutional layers to the encoder so that the information gradient flow obtained by different layers can be reused, and the receptive field can be expanded. In the decoder, based on Jensen-Shannon (JS) divergence, we first recalculate the weight of the feature channels obtained from each layer, which enhances the useful information and suppresses the interference information in the noisy environments to boost the segmentation accuracy. The proposed architecture is validated in the CASIA v4.0 interval (CASIA) and IIT Delhi v1.0 datasets (IITD). For CASIA, the mean error rate is 0.78%, and the F-measure value is 98.21%. For IITD, the mean error rate is 0.97%, and the F-measure value is 97.87%. Experimental results show that the proposed method outperforms other state-of-art methods under noisy environments, such as Gaussian blur, Gaussian noise, and salt and pepper noise.
ISSN:1866-9956
1866-9964
DOI:10.1007/s12559-020-09759-9