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Iris segmentation method based on improved UNet++

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Abstract

With the increasing demand for identity authentication, iris recognition systems are gradually applied in various scenarios. Iris segmentation is the basis of the iris recognition system. Iris images taken in non-ideal situations contain much irrelevant noise. In addition, the quality of iris images varies greatly under different shooting conditions. These factors seriously affect the accuracy of iris segmentation. However, traditional algorithms are not adaptable enough, and the algorithms based on convolutional neural networks (CNNs) are not efficient enough. Inspired by the success of U-shaped network++ (UNet++) in image segmentation, in this paper, an end-to-end encoder-decoder model based on improved UNet++ is proposed to perform the iris segmentation, referred to as Attention Mechanism UNet++ (AM-UNet++). The main contributions are as follows: firstly, EfficientNetV2 is selected as convolutional blocks of UNet++ to improve training speed and reduce the number of network parameters. Secondly, an attention module is embedded into the down-sampling process of UNet++ to suppress irrelevant noise interference and strengthen the ability to learn the discriminability of the iris region. Finally, the algorithm adopts a pruning scheme to get four different performance networks, which can meet the needs of iris recognition in multiple scenarios. The experimental results on two near-infrared illumination iris databases and one visible light illumination iris database demonstrate that the method has good iris segmentation ability and generalization performance. The iris segmentation accuracy and efficiency of the proposed method are higher than the state-of-the-art fusion method.

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References

  1. Ahmad S, Fuller B (2019) Unconstrained Iris segmentation using convolutional neural networks. Presented at the Asian Conference on Computer Vision (ACCV) 11367:450–466. https://doi.org/10.1007/978-3-030-21074-8_36

  2. Uhl Andreas, Wild P (2012) Multi-stage visible wavelength and near infrared Iris segmentation framework. Presented at the International Conference Image Analysis and Recognition (ICIAR) 7325:1–10. https://doi.org/10.1007/978-3-642-31298-4_1

  3. Bazrafkan S, Thavalengal S, Corcoran P (2018) An end to end deep neural network for iris segmentation in unconstrained scenarios. Nural Netw 106:79–95. https://doi.org/10.1016/j.neunet.2018.06.011

    Article  Google Scholar 

  4. Bhadauria HS, Dewal ML (2014) Intracranial hemorrhage detection using spatial fuzzy c-mean and region-based active contour on brain CT imaging. SIViP 8:357–364. https://doi.org/10.1007/s11760-012-0298-0

    Article  Google Scholar 

  5. Bhadauria NS, Kumar I, Bhadauria HS, Patel RB (2021) Hemorrhage detection using edge-based contour with fuzzy clustering from brain computed tomography images. Int J Syst Assur Eng Manag 12:1296–1307. https://doi.org/10.1007/s13198-021-01269-7

    Article  Google Scholar 

  6. Carl M, Goldberg R (2013) Iris biometrics: from segmentation to template security. Comput Rev 54(11):672–673

    Google Scholar 

  7. Chen G, Zhou Y (2004) Iris location based on Hough transform. J East Chin Univ Sci Technol 30(2):230–233. https://doi.org/10.14135/j.cnki.1006-3080.2004.02.025

  8. Chen Y, Wang W, Zeng Z, Wang Y (2019) An adaptive CNNs technology for robust iris segmentation. IEEE Access 7:64517–64532. https://doi.org/10.1109/ACCESS.2019.2917153

    Article  Google Scholar 

  9. Chinese Academy of Science—Institute of Automation, CASIA-IRIS-V4 Iris Image Database Version4.0 (CASIA-IRIS-V4-IrisV4). http://biometrics.idealtest.org/dbDetailForUser.do?id=4. Accessed 10 Dec 2018

  10. Dargan S, Kumar M (2020) A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities. Expert Syst Appl 143:113114. https://doi.org/10.1016/j.eswa.2019.113114

    Article  Google Scholar 

  11. Daugman JG (1993) High confidence visual recognition of persons by a test of statistical independence. IEEE Trans Pattern Anal Mach Intell 15(11):1148–1161. https://doi.org/10.1109/34.244676

    Article  Google Scholar 

  12. Fernando AF, Josef B (2012) Iris boundaries segmentation using the generalized structure tensor. Presented at the IEEE fifth international conference on biometrics: theory, applications and systems (BTAS), pp 426–431. https://doi.org/10.1109/BTAS.2012.6374610

  13. Gupta S, Thakur K, Kumar M (2021) 2D-human face recognition using SIFT and SURF descriptors of face’s feature regions. Vis Comput 37:447–456. https://doi.org/10.1007/s00371-020-01814-8

    Article  Google Scholar 

  14. Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient Mobile network design. Presented at the Conference on Computer Vision and Pattern Recognition (CVPR) arXiv:2103.02907

  15. Hu J, Shen L, Albanie S, Sun G, Wu E (2020) Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell 42(8):2011–2023. https://doi.org/10.1109/TPAMI.2019.2913372

    Article  Google Scholar 

  16. Jain AK, Nandakumar K, Ross A (2016) 50 years of biometric research: accomplishments, challenges, and opportunities. Pattern Recogn Lett 79:80–105. https://doi.org/10.1016/j.patrec.2015.12.013

    Article  Google Scholar 

  17. Jalilian E, Uhl A (2017) Iris segmentation using fully convolutional encoder–decoder networks. Presented at the Advances in Computer Vision and Pattern Recognition (ACVPR) https://doi.org/10.1007/978-3-319-61657-5_6

  18. Kaur P, Kumar R, Kumar M (2019) A healthcare monitoring system using random forest and internet of things (IoT). Multimed Tools Appl 78:19905–19916. https://doi.org/10.1007/s11042-019-7327-8

    Article  Google Scholar 

  19. Kumar A, Arun P (2010) Comparison and combination of iris matchers for reliable personal authentication. Pattern Recogn 43(3):1016–1026. https://doi.org/10.1016/j.patcog.2009.08.016

    Article  MATH  Google Scholar 

  20. Kumar A, Kumar M, Kaur A (2021) Face detection in still images under occlusion and non-uniform illumination. Multimed Tools Appl 80:14565–14590. https://doi.org/10.1007/s11042-020-10457-9

    Article  Google Scholar 

  21. Lian S, Luo Z, Zhong Z, Lin X, Su S, Li S (2018) Attention guided U-Net for accurate iris segmentation. J Vis Commun Image Represent 56:296–304. https://doi.org/10.1016/j.jvcir.2018.10.001

    Article  Google Scholar 

  22. Liu J, Fu X, Wang H (2010) Iris image segmentation based on K-means cluster. Presented at the IEEE international conference on intelligent computing and intelligent systems, pp 194–198. https://doi.org/10.1109/ICICISYS.2010.5658566

  23. Liu N, Li H, Zhang M, Liu J, Sun Z, Tan T (2016) Accurate iris segmentation in non-cooperative environments using fully convolutional networks. Presented at the international conference on biometrics (ICB), pp 1–8. https://doi.org/10.1109/ICB.2016.7550055

  24. Lozej J, Meden B, Struc V, Peer P (2018) End-to-end Iris segmentation using U-net. Presented at the IEEE international work conference on bioinspired intelligence (IWOBI), pp 1–6. https://doi.org/10.1109/IWOBI.2018.8464213

  25. Mayank V, Richa S, Afzel N (2008) Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing. IEEE Trans Syst Man Cybern B Cybern 38(4):1021–1035. https://doi.org/10.1109/TSMCB.2008.922059

    Article  Google Scholar 

  26. Milletari F, Navab N, Ahmadi S (2016) V-Net: fully convolutional neural networks for volumetric medical image segmentation. Presented at the fourth international conference on 3D vision (3DV) pp 565–571. https://doi.org/10.1109/3DV.2016.79

  27. Proenca H, Filipe S, Santos R, Oliveira J, Alexandre LA (2010) 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. https://doi.org/10.1109/TPAMI.2009.66

    Article  Google Scholar 

  28. Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. Presented at the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 9351:234–241. https://doi.org/10.1007/978-3-319-24574-4_28

  29. Roy AG, Navab N, Wachinger C (2018) Concurrent spatial and channel ‘Squeeze & Excitation’ in fully convolutional networks. Presented at the medical image computing and computer assisted intervention (MICCAI) 11070. https://doi.org/10.1007/978-3-030-00928-1_48

  30. Ryan WJ, Woodard DL, Duchowski AT, Birchfield ST (2008) Adapting starburst for elliptical iris segmentation. IEEE Int Conf Biom Theory Appl Syst. https://doi.org/10.1109/BTAS.2008.4699340

  31. Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640–651. https://doi.org/10.1109/TPAMI.2016.2572683

    Article  Google Scholar 

  32. Othman N, Dorizzi B, Garcia-Salicetti S (2016) A biometric reference system for Iris, Osiris Version 4.1 http://svnext.it-sudparis.eu/svnview2-eph/ref_syst/Iris_Osiris_v4.1/

  33. Tan CW, Kumar A (2012) Unified framework for automated iris segmentation using distantly acquired face images. IEEE Trans Image Process 21(9):4068–4079. https://doi.org/10.1109/TIP.2012.2199125

    Article  MathSciNet  MATH  Google Scholar 

  34. Tan M, Le QV (2021) EfficientNetV2: smaller models and faster training. arXiv:2104.00298v3

  35. Uhl A, Wild P (2012) `Weighted adaptive Hough and ellipsopolar transforms for real-time iris segmentation. Presented at the 5th IAPR international conference on biometrics (ICB), pp 283–290. https://doi.org/10.1109/ICB.2012.6199821

  36. Wang Q, Meng X, Sun T, Zhang X (2021) A light iris segmentation network. Vis Comput. https://doi.org/10.1007/s00371-021-02134-1

  37. Wild P, Hofbauer H, Ferryman J, Uhl A (2015) Segmentation-level fusion for Iris recognition, pp 1–6. https://doi.org/10.1109/BIOSIG.2015.7314620

  38. Wildes RP (1997) Iris recognition: an emerging biometric technology. Proc IEEE 85(9):1348–1363. https://doi.org/10.1109/5.628669

    Article  Google Scholar 

  39. Woo S, Park J, Lee JY, Kweon IS (2018) CBAM: convolutional block attention module. Presented at the European Conference on Computer Vision (ECCV) 11211: 3–19. https://doi.org/10.1007/978-3-030-01234-2_1

  40. Yin X, Goudriaan J, Lantinga EA, Vos J, Spiertz HJ (2003) A flexible sigmoid function of determinate growth. Ann Bot 91(3):361–371. https://doi.org/10.1093/aob/mcg029

    Article  Google Scholar 

  41. You X, Zhao P, Mu X, Bai K, Lian S (2021) Heterogeneous noise Iris segmentation based on attention mechanism and dense multi-scale features. Laser Optoelectron Prog 1–19

  42. Zhang W, Lu X, Gu Y, Liu Y, Meng X, Li J (2019) A robust Iris segmentation scheme based on improved U-net. IEEE Access 7:85082–85089. https://doi.org/10.1109/ACCESS.2019.2924464

    Article  Google Scholar 

  43. Zhou R, Shen W (2021) PI-Unet: a neural network model for heterogeneous iris segmentation. Comput Eng Appl (CEA) 57(15):223–229. https://doi.org/10.3778/j.issn.1002-8331.2005-0068

    Article  Google Scholar 

  44. Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2020) UNet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging 39(6):1856–1867. https://doi.org/10.1109/TMI.2019.2959609

    Article  Google Scholar 

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Acknowledgements

The research in this paper uses the CASIA-v4 databases provided by Chinese Academy of Science; IITD iris image database provided by the IIT Delhi, New Delhi, India; UBIRIS.V2 iris database provided by Department of Computer Science, University of Beira Interior.

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Funding

This research was supported by the Science and technology project of the Jilin Provincial Education Department under Grant No. JJKH20220118KJ.

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Authors and Affiliations

  1. School of Computer Science, Northeast Electric Power University, Jilin, 132012, China

    Guang Huo & Dawei Lin

  2. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Meng Yuan

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  1. Guang Huo
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Correspondence to Dawei Lin.

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Huo, G., Lin, D. & Yuan, M. Iris segmentation method based on improved UNet++. Multimed Tools Appl 81, 41249–41269 (2022). https://doi.org/10.1007/s11042-022-13198-z

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