Adaptive Initial Contour and Partly-Normalization Algorithm for Iris Segmentation of Blurry Iris Images

Authors

  • Shahrizan Jamaludin Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Malaysia
  • Ahmad Faisal Mohamad Ayob Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Malaysia
  • Syamimi Mohd Norzeli Faculty of Innovative Design and Technology, Universiti Sultan Zainal Abidin, Malaysia
  • Saiful Bahri Mohamed Faculty of Innovative Design and Technology, Universiti Sultan Zainal Abidin, Malaysia

DOI:

https://doi.org/10.32890/jict2022.21.3.5

Keywords:

Iris segmentation, adaptive initial contour, adaptive Chan-Vese active contour, partly-normalization, segmentation accuracy

Abstract

Iris segmentation is a process to isolate the accurate iris region from the eye image for iris recognition. Iris segmentation on non-ideal and noisy iris images is accurate with active contour. Nevertheless, it is currently unclear on how active contour responds to blurry iris images or motion blur, which presents a significant obstacle in iris segmentation. Investigation on blurry iris images, especially on the initial contour position, is rarely published and must be clarified. Moreover, evolution or convergence speed remains a significant challenge for active contour as it segments the precise iris boundary. Therefore, this study carried out experiments to achieve an efficient iris segmentation algorithm in terms of accuracy and fast execution, according to the aforementioned concerns. In addition, initial contour was explored to clarify its position. In order to accomplish these goals, the Wiener filter and morphological closing were used for preprocessing and reflection removal. Next, the adaptive initial contour (AIC), δ, and stopping function were integrated to create the adaptive Chan-Vese active contour (ACVAC) algorithm. Finally, the partly -normalization method for normalization and feature extraction was designed by selecting the most prominent iris features. The findings revealed that the algorithm outperformed the other active contour-based approaches in computational time and segmentation accuracy. It proved that in blurry iris images, the accurate initial contour position could be established. This algorithm is significant to solve inaccurate segmentation on blurry iris images.

References

Abdullah, M. A., Dlay, S. S., Woo, W. L., & Chambers, J. A. (2016). Robust iris segmentation method based on a new active contour force with a noncircular normalization. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(12), 3128–3141. https://doi.org/10.1109/TSMC.2016.2562500

Akinfende, A. S., Imoize, A. L., & Ajose, S. O. (2020). Investigation of iris segmentation techniques using active contours for non-cooperative iris recognition. Indonesian Journal of Electrical Engineering and Computer Science, 19(3), 1275–1286. https://doi.org/10.11591/ijeecs.v19.i3.pp1275-1286

Alsmirat, M. A., Al-Alem, F., Al-Ayyoub, M., Jararweh, Y., & Gupta, B. (2019). Impact of digital fingerprint image quality on the fingerprint recognition accuracy. Multimedia Tools and Applications, 78(3), 3649–3688. https://doi.org/10.1007/ s11042-017-5537-5

Amreen, S., Mockus, A., Zaretzki, R., Bogart, C., & Zhang, Y. (2020). ALFAA: Active learning fingerprint based anti-aliasing for correcting developer identity errors in version control systems. Empirical Software Engineering, 25(2), 1136–1167. https://doi.org/10.1007/s10664-019-09786-7 Journal of ICT, 21, No. 3 (July) 2022, pp: 411–

Anne, N., Dunbar, M. D., Abuna, F., Simpson, P., Macharia, P., Betz, B., & Carey, F. (2020). Feasibility and acceptability of an iris biometric system for unique patient identification in routine HIV services in Kenya. International Journal of Medical Informatics, 133, 104006. https://doi.org/10.1016/j. ijmedinf.2019.104006

Arsalan, M., Hong, H. G., Naqvi, R. A., Lee, M. B., Kim, M. C., Kim, D. S., Kim, C. S., & Park, K. R. (2017). Deep learning-based iris segmentation for iris recognition in visible light environment. Symmetry, 9(11), 263. https://doi.org/10.3390/ sym9110263

Arsalan, M., Kim, D. S., Lee, M. B., Owais, M., & Park, K. R. (2019). FRED-Net: Fully residual encoder–decoder network for accurate iris segmentation. Expert Systems with Applications, 122, 217–241. https://doi.org/10.1016/j.eswa.2019.01.010

Arsalan, M., Naqvi, R. A., Kim, D. S., Nguyen, P. H., Owais, M., & Park, K. R. (2018). 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. https://doi.org/10.3390/s18051501

Azimi, M., Rasoulinejad, S. A., & Pacut, A. (2019). Iris recognition under the influence of diabetes. Biomedical Engineering/ Biomedizinische Technik, 64(6), 683–689. https://doi.org/10.1515/bmt-2018-0190

Barpanda, S. S., Majhi, B., Sa, P. K., Sangaiah, A. K., & Bakshi, S. (2019). Iris feature extraction through wavelet mel-frequency cepstrum coefficients. Optics and Laser Technology, 110, 13–23. https://doi.org/10.1016/j.optlastec.2018.03.002

Baselice, F., Ferraioli, G., Ambrosanio, M., Pascazio, V., & Schirinzi, G. (2018). Enhanced wiener filter for ultrasound image restoration. Computer Methods and Programs in Biomedicine, 153, 71–81. https://doi.org/10.1016/j. cmpb.2017.10.006

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

Blasco, J., & Peris-Lopez, P. (2018). On the feasibility of low-cost wearable sensors for multi-modal biometric verification. Sensors, 18(9), 2782. https://doi.org/10.3390/ s18092782 Journal of ICT, 21, No. 3 (July) 2022, pp: 411–

Chan, T. F., & Vese, L. A. (2001). Active contours without edges. IEEE Transactions on Image Processing, 10(2), 266–277. https://doi.org/10.1109/83.902291

Chang, Y. T., Shih, T. K., Li, Y. H., & Kumara, W. G. C. W. (2020). Effectiveness evaluation of iris segmentation by using geodesic active contour (GAC). The Journal of Supercomputing, 76(3), 1628–1641. https://doi.org/10.1007/s11227-018-2450-2

Chen, Y., Liu, Y., & Zhu, X. (2015). Robust iris segmentation algorithm based on self-adaptive Chan-Vese level set model. Journal of Electronic Imaging, 24(4), 043012. https://doi.org/10.1117/1. JEI.24.4.043012

Chen, Y., Wu, C., & Wang, Y. (2020). T-center: A novel feature extraction approach towards large-scale iris recognition. IEEE Access, 8, 32365–32375. https://doi.org/10.1109ACCESS.2020.2973433

Cohen, F., Sowmithran, S., & Li, C. (2021). 3D iris model and reader for iris identification. Concurrency and Computation: Practice and Experience, 33(12), e5653. https://doi.org/10.1002/ cpe.5653

De Marsico, M., Nappi, M., Narducci, F., & Proenca, H. (2018). Insights into the results of miche i-mobile iris challenge evaluation. Pattern Recognition, 74, 286–304. https://doi.org/10.1016/j.patcog.2017.08.028

Ding, K., Xiao, L., & Weng, G. (2017). Active contours driven by region-scalable fitting and optimized Laplacian of Gaussian energy for image segmentation. Signal Processing, 134, 224–233. https://doi.org/10.1016/j.sigpro.2016.12.021

Djekoune, A. O., Messaoudi, K., & Amara, K. (2017). Incremental circle Hough transform: An improved method for circle detection. Optik, 133, 17–31. https://doi.org/10.1016/j. ijleo.2016.12.064

Duan, Y., Peng, T., & Qi, X. (2020). Active contour model based on LIF model and optimal DoG operator energy for image segmentation. Optik, 202, 163667. https://doi.org/10.1016/j. ijleo.2019.163667

Fang, J., Liu, H., Zhang, L., Liu, J., & Liu, H. (2019). Active contour driven by weighted hybrid signed pressure force for image segmentation. IEEE Access, 7, 97492–97504. https://doi.org/10.1109/ACCESS.2019.2929659

Hossain, M. S., Balagani, K. S., & Phoha, V. V. (2021). Effectiveness of symmetric rejection for a secure and user convenient multistage Journal of ICT, 21, No. 3 (July) 2022, pp: 411– biometric system. Pattern Analysis and Applications, 24(1), 49–60. https://doi.org/10.1007/s10044-020-00899-0

Jamaludin, S., Zainal, N., & Zaki, W. M. D. W. (2017). GPU implementation of sub-iris technique in iris recognition system. Pertanika Journal of Science and Technology, 25(S)3, 263–274.

Jamaludin S., Zainal N., & Zaki W. M. D. W. (2016). Iris recognition based on the modified Chan-Vese active contour. Jurnal Teknologi,78(10–3),13–17.https://doi.org/10.11113/ jt.v78.9756

Jamaludin, S., Zainal, N., & Zaki, W. M. D. W. (2021). Deblurring of noisy iris images in iris recognition. Bulletin of Electrical Engineering and Informatics, 10(1), 156–159. https://doi.org/10.11591/eei.v10i1.2467

Jan, F. (2017). Segmentation and localization schemes for non-ideal iris biometric systems. Signal Processing, 133, 192–212. https://doi.org/10.1016/j.sigpro.2016.11.007

Jin, R., & Weng, G. (2019). A robust active contour model driven by fuzzy c-means energy for fast image segmentation. Digital Signal Processing, 90, 100–109. https://doi.org/10.1016/j. dsp.2019.04.004

Kaur, B. (2020). Iris spoofing detection using discrete orthogonal moments. Multimedia Tools and Applications, 79(9), 6623–6647. https://doi.org/10.1007/s11042-019-08281-x

Khalaf, E. T., Mohammad, M. N., & Moorthy, K. (2018). Robust partitioning and indexing for iris biometric database based on local features. IET Biometrics, 7(6), 589–597. https://doi.org/10.1049/iet-bmt.2017.0130

Ma, C., Luo, G., & Wang, K. (2018). Concatenated and connected random forests with multiscale patch driven active contour model for automated brain tumor segmentation of MR images. IEEE Transactions on Medical Imaging, 37(8), 1943–1954. https://doi.org/10.1109/TMI.2018.2805821

Mumford, D., & Shah, J. (1989). Optimal approximations by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics, 42(5), 577–685.

Ouabida, E., Essadique, A., & Bouzid, A. (2017). Vander Lugt correlator based active contours for iris segmentation and tracking. Expert Systems with Applications, 71, 383–395. https://doi.org/10.1016/j.eswa.2016.12.001 Journal of ICT, 21, No. 3 (July) 2022, pp: 411–

Pavaloi, I., & Ignat, A. (2019). Iris image classification using SIFT features. Procedia Computer Science, 159, 241–250. https://doi.org/10.1016/j.procs.2019.09.179

Proenca, H., & Neves, J. C. (2018). Deep-PRWIS: Periocular recognition without the iris and sclera using deep learning frameworks. IEEE Transactions on Information Forensics and Security, 13(4), 888–896. https://doi.org/10.1109/ TIFS.2017.2771230

Quintero, A. V., Perez-Merino, P., & De Smet, H. (2020). Artificial iris performance for smart contact lens vision correction applications. Scientific Reports, 10(1), 1–12. https://doi.org/10.1038/s41598-020-71376-1

Rapaka, S., & Kumar, P. R. (2018). Efficient approach for non-ideal iris segmentation using improved particle swarm optimisation-based multilevel thresholding and geodesic active contours. IET Image Processing, 12(10), 1721–1729. https://doi.org/10.1049/ iet-ipr.2016.0917

Sarier, N. D. (2021). Comments on biometric-based non-transferable credentials and their application in blockchain-based identity management. Computers and Security, 105, 102243. https://doi.org/10.1016/j.cose.2021.102243

Shah, S., & Ross, A. (2009). Iris segmentation using geodesic active contours. IEEE Transactions on Information Forensics and Security, 4(4), 824–836. https://doi.org/10.1109/ TIFS.2009.2033225

Shin, J., Kim, T., Lee, B., & Yang, S. (2017). IRIS-HiSA: Highly scalable and available carrier-grade SDN controller cluster. Mobile Networks and Applications, 22(5), 894–905. https://doi.org/10.1007/s11036-017-0853-6

Shrivastava, H., & Tcheslavski, G. V. (2018). On the potential of EEG for biometrics: Combining power spectral density with a statistical test. International Journal of Biometrics, 10(1), 52–64. https://dx.doi.org/10.1504/IJBM.2018.090128

Sujatha, E., & Chilambuchelvan, A. (2018). Multimodal biometric authentication algorithm using iris, palm print, face and signature with encoded dwt. Wireless Personal Communications, 99(1), 23–34. https://doi.org/10.1007/s11277-017-5034-1

Vyas, R., Kanumuri, T., Sheoran, G., & Dubey, P. (2019). Efficient iris recognition through curvelet transform and polynomial fitting. Optik, 185, 859–867. https://doi.org/10.1016/j. ijleo.2019.04.015 Journal of ICT, 21, No. 3 (July) 2022, pp: 411–

Wang, K., & Kumar, A. (2019). Cross-spectral iris recognition using CNN and supervised discrete hashing. Pattern Recognition, 86, 85–98. https://doi.org/10.1016/j.patcog.2018.08.010

Wang, Q., Yue, W. W. S., Jiang, Z., Xue, T., Kang, S. H., Bergles, D. E., Mikoshiba, K., Offermanns, S., & Yau, K. W. (2017). Synergistic signaling by light and acetylcholine in mouse iris sphincter muscle. Current Biology, 27(12), 1791–1800. https://doi.org/10.1016/j.cub.2017.05.022

Wu, X., & Zhao, L. (2019). Study on iris segmentation algorithm based on dense U-Net. IEEE Access, 7, 123959–123968. https://doi.org/10.1109/ACCESS.2019.2938809

Yu, H., He, F., Pan, Y. (2018). A novel region-based active contour model via local patch similarity measure for image segmentation. Multimedia Tools and Applications, 77(18), 24097–24119. https://doi.org/10.1007/s11042-018-5697-y

Zhang, M., He, Z., Zhang, H., Tan, T., & Sun, Z. (2019). Toward practical remote iris recognition: A boosting based framework. Neurocomputing, 330, 238–252. https://doi.org/10.1016/j.neucom.2017.12.053

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

Zhao, Z., & Kumar, A. (2018). Improving periocular recognition by explicit attention to critical regions in deep neural network. IEEE Transactions on Information Forensics and Security, 13(12), 2937–2952. https://doi.org/10.1109/TIFS.2018.2833018

Downloads

Published

17-07-2022

Issue

Vol. 21 No. 3 (2022): Journal of Information and Communication Technology (JICT) Vol. 21, No.3 July 2022

Section

Articles

License

Copyright (c) 2022 Journal of Information and Communication Technology

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Shahrizan Jamaludin, Mohamad Ayob, A. F., Syamimi Mohd Norzeli, & Saiful Bahri Mohamed. (2022). Adaptive Initial Contour and Partly-Normalization Algorithm for Iris Segmentation of Blurry Iris Images. Journal of Information and Communication Technology, 21(3), 411-435. https://doi.org/10.32890/jict2022.21.3.5

Research impact

Harvested 2026-09-11
11 citations, from Scopus — the highest of the sources checked

Counts differ between services because each indexes a different body of literature. None of them is the whole picture.

Identifiers DOI 10.32890/jict2022.21.3.5 OpenAlex W4285803681 Scopus 85134778497