Skip to main content
SpringerLink
Log in

An iris segmentation scheme based on bendlets

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Due to the effect of agents such as ambiance, transition channel, and other agents, images are polluted by noise during collection, transition, and compaction, leading to decrease image quality. Noise can decrease the accuracy of the next stages of image processing systems. Therefore, one of the vital stages in the novel processing systems is denoising. This article offers a novel image denoising approach using bendlets. Other multi-scale transformations (such as wavelets, curvelets, and shearlets) cannot recognize properties such as location, direction, and curvature of discontinuities well in piecewise stable images. To solve this problem, bendlets are suggested in this article. Bendlets differ from other multi-scale transformations in that an additional bending parameter is utilized for recognizing the curvature of discontinuities. Bendlets need a fewer number of coefficients to identify curvatures than other multi-scale transformations. Furthermore, they help to make the edges more obvious. The suggested approach is utilized on the UBIRIS.V2 database. It earns better accuracy and stability than other multi-scale transformations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data Availability

The datasets analyzed during the current study are available in the MMU, IITD and CASIA repositories, cs.princeton.edu, iitd.ac.in and cbsr.ia.ac.cn, respectively.

References

  1. Daugman, J.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1148–1161 (1993)

    Article  Google Scholar 

  2. Fan, L., Li, X., Guo, Q., Zhang, C.: Nonlocal image denoising using edge-based similarity metric and adaptive parameter selection. Sci. China Inf. Sci. 61(4), 049101 (2018). https://doi.org/10.1007/s11432-017-9207-9

    Article  Google Scholar 

  3. Gu, S., Zhang, L., Zuo, W., Feng, X.: Weighted nuclear norm minimization with application to image denoising. In: Abstracts of 2014 IEEE conference on computer vision and pattern recognition. IEEE Columbus, pp. 2862–2869 (2014). https://doi.org/10.1109/CVPR.2014.366

  4. Al-Ameen, Z., Al-Ameen, S., Sulong, G.: Latest methods of image enhancement and restoration for computed tomography: a concise review. Appl. Med. Inf. 36(1), 1–12 (2015)

    Google Scholar 

  5. Benesty, J., Chen, J., Huang, Y.: Study of the widely linear wiener filter for noise reduction. In: Abstracts of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 205–208. IEEE, Dallas (2010). https://doi.org/10.1109/ICASSP.2010.5496033

  6. Li, X., Hu, Y., Gao, X., Tao, D., Ning, B.: A multi-frame image super-resolution method. Signal Process. 90(2), 405–414 (2010). https://doi.org/10.1016/j.sigpro.2009.05.028

    Article  Google Scholar 

  7. Zhang, K., Zuo, W., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN-based image denoising. IEEE Trans. Image Process. 27(9), 4608–4622 (2018). https://doi.org/10.1109/TIP.2018.2839891

    Article  MathSciNet  Google Scholar 

  8. Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017). https://doi.org/10.1109/TIP.2017.2662206

    Article  MathSciNet  PubMed  Google Scholar 

  9. Cruz, C., Foi, A., Katkovnik, V., Egiazarian, K.: Nonlocality-reinforced convolutional neural networks for image denoising. IEEE Signal Process. Lett. 25(8), 1216–1220 (2018). https://doi.org/10.1109/LSP.2018.2850222

    Article  Google Scholar 

  10. Prashar, N., Sood, M., Jain, S.: Design and implementation of a robust noise removal system in ECG signals using dual-tree complex wavelet transform. Biomed. Signal Process. Control. 63, 102212 (2021). https://doi.org/10.1016/j.bspc.2020.102212

    Article  Google Scholar 

  11. Hyder, S.A., Sukanesh, R.: An efficient algorithm for denoising MR and CT images using digital curvelet transform. In: Software Tools and Algorithms for Biological Systems. pp. 471–480. Springer, New York (2011)

  12. Mao, X., Shen, C., Yang, Y.B.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. Neural Inf. Process Syst. (NIPS) 1, 2802–2810 (2016)

    Google Scholar 

  13. Liu, P., Zhang, H., Zhang, K., Lin, L., Zuo, W., Multi-level wavelet-CNN for image restoration. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 773–782 (2018)

  14. Yan, C., Li, Z., Zhang, Y., Liu, Y., Ji, X., Zhang, Y.: Depth image denoising using nuclear norm and learning graph model. ACM Trans. Multimed. Comput. Commun. Appl. 16(4), 1–17 (2020)

    Article  Google Scholar 

  15. Guo, S., Yan, Z., Zhang, K., Zuo, W., Zhang, L.: Toward convolutional blind denoising of real photographs (2018). arXiv:1807.04686

  16. Lyu, Z., Zhang, C., Han, M.: DSTnet: a new discrete shearlet transform-based CNN model for image denoising. Multimedia Syst. 27, 1165–1177 (2021). https://doi.org/10.1007/s00530-021-00753-1

    Article  Google Scholar 

  17. Chen, Y., Wu, C., Wang, Y.: Whether normalized or not? Towards more robust iris recognition using dynamic programming. Image Vis. Comput. 107, 104112 (2021). https://doi.org/10.1016/j.imavis.2021.104112

    Article  Google Scholar 

  18. Cantoni, V., Cascone, L., Nappi, M., Porta, M.: Demographic classification through pupil analysis. Image Vis. Comput. 102, 103980 (2020)

    Article  Google Scholar 

  19. Singh, A., Arora, A., Nigam, A.: Cancelable iris template generation by aggregating patch level ordinal relations with its holistically extended performance and security analysis. Image Vis. Comput. 104, 104017 (2020)

    Article  Google Scholar 

  20. Reddy, N., Rattani, A., Derakhshani, R.: Generalizable deep features for ocular biometrics. Image Vis. Comput. 103, 103996 (2020). https://doi.org/10.1016/j.imavis.2020.103996

    Article  Google Scholar 

  21. Raja, K., Ramachandra, R., Busch, C.: Collaborative representation of blur invariant deep sparse features for periocular recognition from smartphones. Image Vis. Comput. 101, 103979 (2020). https://doi.org/10.1016/j.imavis.2020.103979

    Article  Google Scholar 

  22. Donida Labati, R., Muñoz, E., Piuri, V., Oss, A., Scotti, F.: Non-ideal iris segmentation using Polar Spline RANSAC and illumination compensation. Comput. Vis. Image Understand. 188, 1–17 (2019)

    Article  Google Scholar 

  23. Jan, F., Min-Allah, N.: An effective iris segmentation scheme for noisy images. Biocybern. Biomed. Eng. 40(3), 1064–1080 (2020)

    Article  Google Scholar 

  24. Ma, L., Li, H., Yu, K.: Fast iris localization algorithm on noisy images based on conformal geometric algebra. Digit Signal Process. 100, 102682 (2020)

    Article  Google Scholar 

  25. MMU\(_{-}\)Iris\(_{-}\)Database. https://www.cs.princeton.edu/ andyz/downloads/MMUIrisDatabase.zip

  26. IITD\(_{-}\)iris\(_{-}\)databases. http://www.iitd.ac.in/. Accessed 02 Aug 19

  27. CASIA\(_{-}\)Iris\(_{-}\)Database. http://www.cbsr.ia.ac.cn/english/IrisDatabase.asp

  28. Sardar, M., Mitra, S., Shankar, B.U.: Iris localization using rough entropy and CSA: a soft computing approach. Appl. Soft Comput. 67, 61–69 (2018). https://doi.org/10.1016/j.asoc.2018.02.047

    Article  Google Scholar 

  29. Soliman, N.F., et al.: Efficient iris localization and recognition. Optik 140, 469–475 (2017). https://doi.org/10.1016/j.ijleo.2016.11.150

    Article  Google Scholar 

  30. Metaphor, H., Sa, P.K., Majhi, B.: Fast segmentation and adaptive SURF descriptor for iris recognition. Math. Comput. Model. 58, 132–146 (2013)

    Article  Google Scholar 

  31. Boonchuan, T., Setumin, S., Radman, A., Suandi, S.: Efficient iris and eyelids detection from facial sketch images. Electron Lett. Comput. Vis. Image Anal. 17(1), 16–28 (2018)

    Google Scholar 

  32. Ahad, M.A.R., et al.: A study on face detection using violajones algorithm for various backgrounds. Angels and distances. Appl. Soft Comput. 5, 25 (2018)

    Google Scholar 

  33. Jan, F.: Non-circular iris contours localization in the visible wavelength eye images. Comput. Electr. Eng. 62, 166–177 (2017)

    Article  Google Scholar 

  34. Zaman Khan, T., Podder, P., Foisal Hossain, MD.: Fast and efficient iris segmentation approach based on morphology and geometry operation. In: International Conference on Software, Knowledge, Information Management and Application, 18–20 Dec, Dhaka, Bangladesh (2014)

  35. Cao, L., Zhou, Y., Yan, F., Tian, Y.: A novel iris segmentation approach based on superpixel method. In: Fourth International Conference on Instrumentation and Measurement, Computer, Communication and Control, pp. 18–20 Sep, Harbin, China (2014)

  36. Abdullah, M.A., Dlay, S.S., Woo, W.L., Chambers, J.A.: Robust iris segmentation method based on a new active contour force with a noncircular normalization. IEEE Trans. Syst. Man Cybern. Syst. 47(12), 1–14 (2017)

    Article  Google Scholar 

  37. Mirzafam, M., Aghazadeh, N.: A three-stage shearlet-based algorithm for vessel segmentation in medical imaging. Pattern Anal. Appl. 24, 591–610 (2021). https://doi.org/10.1007/S10044-020-00915-3

    Article  Google Scholar 

  38. Sharafyan Cigaroudy, L., Aghazadeh, N.: A new multiphase segmentation method using eigenvectors based on K real numbers. Circuits Syst. Signal Process. 36(4), 1445–1454 (2017). https://doi.org/10.1007/S00034-016-0359-7

    Article  MathSciNet  Google Scholar 

  39. Sharafyan Cigaroudy, L., Aghazadeh, N.: A multiphase segmentation method based on binary segmentation method for Gaussian noisy image. SIViP 11(5), 825–831 (2017). https://doi.org/10.1007/S11760-016-1028-9

    Article  Google Scholar 

  40. Abdani, S.P., Zaki, W.M.D.W., Mustapha, A., Hussain, A.: Iris segmentation method of pterygium anterior segment photographed image. In: IEEE Symposium on Computer Applications & Industrial Electronics, 12–14 Apr, Langkawi, Malaysia (2015)

  41. Iqbal Mozumder, A., Ara Begum, S.H.: Iris segmentation using Adaptive Histogram Equalization and median filtering. In: International Symposium on Advanced Computing and Communication, 14–15 Sep, Silchar, India (2015)

  42. Zhao, Z., Kumar, A.: An accurate iris segmentation framework under relaxed imaging constraints using total variation model. In: IEEE International Conference on Computer Vision, 7–13 Des, Santiago, Chile (2015)

  43. Liu, N., Li, H., Zhang, M., Liu, J., Sun, Z., Tan, T.: Accurate iris segmentation in non-cooperative environments using fully convolutional networks. In: International Conference on Biometrics, 12–13 Jun, Halmstad, Sweden (2016)

  44. Routray, S., Ray, A.K., Mishra, C.: Image denoising by preserving geometric components based on weighted bilateral filter and curvelet transform. Optik 159, 333–343 (2018)

    Article  Google Scholar 

  45. Aghazadeh, N., Noras, P.: New denoising and edge detection scheme based on rationalized Haar functions. J. Mach. Vis. Image Process. 5(1), 99–111 (2018)

    Google Scholar 

  46. Om, H., Biswas, M.: A generalized image denoising method using neighbouring wavelet coefficients. SIViP 9(1), 191–200 (2015)

    Article  Google Scholar 

  47. Goyal, B., Dogra, A., Sangaiah, A.K.: An effective nonlocal means image denoising framework based on non-subsampled shearlet transform. Soft. Comput. (2022). https://doi.org/10.1007/s00500-022-06845-y

    Article  Google Scholar 

  48. Lyu, Z., Chen, Y., Hou, Y., Zhang, C.: Toward a nonsubsampled shearlet transform for broad convolutional neural network image denoising. Digital Signal Process. (2020). https://doi.org/10.1016/j.dsp.2022.103407

    Article  Google Scholar 

  49. Ansari, R.A., Buddhiraju, K.M.: Erratum to: A comparative evaluation of denoising of remotely sensed images using wavelet, curvelet, and contourlet transforms. J. Indian Soc. Remote Sens. 45, 193 (2017). https://doi.org/10.1007/s12524-016-0579-0

    Article  Google Scholar 

  50. Lessig, C., Petersen, P., Schafer, M.: A second-order shearlet transform with bent elements. Appl. Comput. Harmonic Anal. 46, 384–399 (2019)

    Article  MathSciNet  Google Scholar 

  51. Kutyniok, G., Lim, W., Steidl, G.: Shearlets: theoty and applications (2014). https://doi.org/10.1002/gamm.201410012

  52. Kutyniok, G., Labate, D.: Introduction to shearlets. In: Kutyniok, G., Labate, D. (eds.) Shearlets Applied and Numerical Harmonic Analysis. Birkhuser, Boston (2012)

    Google Scholar 

  53. Dahlke, S., Kutyniok, G., Steidl, G., Teschke, G.: Shearlet coorbit spaces and associated Banach frames. Appl. Comput. Harmon. Anal. 27(2), 195–214 (2009)

    Article  MathSciNet  Google Scholar 

  54. Dahlke, S., Kutyniok, G., Maass, P., Sagiv, C., Stark, H.G., Teschke, G.: The uncertainty principle associated with the continuous shearlet transform. Int. J. Wavelets Multiresolution Inf. Process. 6(2), 157–181 (2008)

    Article  MathSciNet  Google Scholar 

  55. Proença, 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). https://doi.org/10.1109/TPAMI.2009.66

    Article  PubMed  Google Scholar 

  56. Ito, Y., Ohyama, W., Wakabayashi, T., Kimura, F.: Detection of eyes by circular hough transform and histogram of gradient. In: 21st International Conference on Pattern Recognition, Japan, pp. 11–15 (2012)

  57. Daugman, J.: How iris recognition works. Paper Presented at the International Conference on Image Processing, vol. 1, pp. 33–36 (2002)

  58. Proenca, H., Alexandre, L.A.: The NICE.I: noisy iris challenge evaluation-part I. In: Proceedings of the 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems, Crystal City, VA, USA, 27–29 September, pp. 1–1535 (2007)

  59. Rathgeb, C.: Iris biometrics from segmentation to template security. Comput. Rev. 54, 672–673 (2013)

    Google Scholar 

  60. Wild, P., Hofbauer, H., Ferryman, J., Uhl, A.: Segmentation-level fusion for iris recognition. In: Proceedings of the 2015 International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany, 9–11 September, pp. 1–6 (2015)

  61. Uhl, A., Wild, P.: Weighted adaptive Hough and ellipsopolar transforms for real-time iris segmentation. In: Proceedings of the 2012 5th IAPR International Conference on Biometrics (ICB), New Delhi, India, 29 March–1 April, pp. 283–290 (2012)

  62. Sutra, G., Dorizzi, B., Garcia-Salicetti, S., Othman, N.: A biometric reference system for iris, Osiris Version 4.1. Accessed 1 Sept 2022

  63. Uhl, A., Wild, P.: Multi-stage visible wavelength and near infrared iris segmentation framework. In: Proceedings of the 9th International Conference, ICIAR 2012: Image Analysis and Recognition, Aveiro, Portugal, pp. 25–27 (2012)

  64. Jalilian, E., Uhl, A.: Iris segmentation using fully convolutional encoder-decoder networks. In: Advances in Computer Vision and Pattern Recognition, pp. 133–155. Springer, Berlin (2017)

  65. You, X., Zhao, P., Mu, X., Bai, K., Lian, S.: Heterogeneous noise iris segmentation based on attention mechanism and dense multiscale features. Laser Optoelectron. Prog. 59, 0410006 (2022)

    Google Scholar 

Download references

Funding

No funding was received to assist with the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Mathematics, Izmir Institute of Technology, Izmir, Türkiye

    Nasser Aghazadeh

  2. Image Processing Laboratory, Department of Applied Mathematics, Azarbaijan Shahid Madani University, Tabriz, Iran

    Nasser Aghazadeh, Mandana Abbasi & Parisa Noras

Authors
  1. Nasser Aghazadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mandana Abbasi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Parisa Noras
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nasser Aghazadeh.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aghazadeh, N., Abbasi, M. & Noras, P. An iris segmentation scheme based on bendlets. SIViP 18, 2683–2693 (2024). https://doi.org/10.1007/s11760-023-02940-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-023-02940-1

Keywords

Search

Navigation