Abstract
As a key step in the iris recognition process, iris segmentation directly affects the accuracy of iris recognition. How to achieve accurate iris segmentation under various environmental conditions is a big challenge. This paper proposes attention skip connection dense network (ASCDNet), which adopts an codec structure, and uses dense blocks as a component of encoder to obtain richer iris features and alleviate the problem of gradient disappearance. In the improved skip connection, channel attention and spatial attention mechanisms are introduced to achieve effective information fusion through the connection of high and low layers. The experimental results on two iris datasets IITD, CASIA-Interval-V4 collected under near-infrared light and one iris dataset UBIRIS.V2 collected under visible light show that the proposed improved skip connection can effectively improve the performance of iris segmentation, the accuracy rates of segmentation are as high as 0.9882, 0.9904, 0.9941, respectively, outperforming most state-of-the-art iris segmentation networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Shabab, B., Shejin, T., Peter, C.: An end to end deep neural network for iris segmentation in unconstraint scenarios. Neural Netw. Official J. Int. Neural Netw. Soc. 106, 79–95 (2017)
Arsalan, M., Naqvi, R., Kim, D., Nguyen, P., Owais, M., Park, K.: IrisDenseNet: robust iris segmentation using densely connected fully convolutional networks in the images by visible light and near-infrared light camera sensors. Sensors 18(5), 1501–1531 (2018)
Zhang, W., Xiaoqi, L., Yu, G., Liu, Y., Meng, X., Li, J.: A robust iris segmentation scheme based on improved U-Net. IEEE Access 7, 85082–85089 (2019). https://doi.org/10.1109/ACCESS.2019.2924464
Chen, Y., Wang, W., Zeng, Z., Wang, Y.: An adaptive CNNs technology for robust iris segmentation. IEEE Access 7, 64517–64532 (2019). https://doi.org/10.1109/ACCESS.2019.2917153
Arsalan, M., Kim, D.S., Lee, M.B., Owais, M., Park, K.R.: FRED-Net: fully residual encoder–decoder network for accurate iris segmentation. Expert Syst. Appl. 122, 217–241 (2019)
Hao, K., Feng, G., Ren, Y., Zhang, X.: Iris segmentation using feature channel optimization for noisy environments. Cogn. Comput. 12(9), 1205–1216 (2020)
You, X., Zhao, P., Mu, X., Bai, K., Lian, S.: Heterogeneous noise lris segmentation based on attention mechanism and dense multiscale feaures. Laser Optoelectron. Prog. 59(04), 109–120 (2022)
Hu J, Shen L, Sun G.: Squeeze‐and‐excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1
Kumar, A., Passi, A.: Comparison and combination of iris matchers for reliable personal authentication. Pattern Recogn. 43(3), 1016–1026 (2010)
Chinese Academy of Sciences Institute of Automation. Casia iris image databases. http://www.cbsr.ia.ac.cn/china/Iris%20Databases%20CH.asp. Accessed 21 May 2020
Proenca, H., Filipe, S., Santos, R., Oliveira, J., Alexandre, L.A.: The UBIRIS.v2: a database of visible wavelength iris images captured on-the-move and at-a-distance. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1529–1535 (2010)
Hofbauer, H., Alonso-Fernandez, F., Wild, P., Bigun, J., Uhl, A.: A ground truth for iris segmentation. In: 2014 22nd International Conference on Pattern Recognition, pp. 527–532 (2014)
Eigen, D., Fergus, R.: Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2650–2658 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Guo, S., Chen, Y., Zeng, Y., Xu, L. (2022). Attention Skip Connection Dense Network for Accurate Iris Segmentation. In: Deng, W., et al. Biometric Recognition. CCBR 2022. Lecture Notes in Computer Science, vol 13628. Springer, Cham. https://doi.org/10.1007/978-3-031-20233-9_41
Download citation
DOI: https://doi.org/10.1007/978-3-031-20233-9_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20232-2
Online ISBN: 978-3-031-20233-9
eBook Packages: Computer ScienceComputer Science (R0)