{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:10:13Z","timestamp":1783570213964,"version":"3.55.0"},"reference-count":46,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,18]],"date-time":"2021-02-18T00:00:00Z","timestamp":1613606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Technology in Taiwan","award":["Ministry of Science and Technology in Taiwan"],"award-info":[{"award-number":["Ministry of Science and Technology in Taiwan"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Iris segmentation plays an important and significant role in the iris recognition system. The prerequisite for accurate iris recognition is the correctness of iris segmentation. However, the efficiency and robustness of traditional iris segmentation methods are severely challenged in a non-cooperative environment because of unfavorable factors, for instance, occlusion, blur, low resolution, off-axis, motion, and specular reflections. All of the above factors seriously reduce the accuracy of iris segmentation. In this paper, we present a novel iris segmentation algorithm that localizes the outer and inner boundaries of the iris image. We propose a neural network model called \u201cInterleaved Residual U-Net\u201d (IRUNet) for semantic segmentation and iris mask synthesis. The K-means clustering is applied to select saliency points set in order to recover the outer boundary of the iris, whereas the inner border is recovered by selecting another set of saliency points on the inner side of the mask. Experimental results demonstrate that the proposed iris segmentation algorithm can achieve the mean IOU value of 98.9% and 97.7% for inner and outer boundary estimation, respectively, which outperforms the existing approaches on the challenging CASIA-Iris-Thousand database.<\/jats:p>","DOI":"10.3390\/s21041434","type":"journal-article","created":{"date-parts":[[2021,2,18]],"date-time":"2021-02-18T21:59:58Z","timestamp":1613685598000},"page":"1434","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Robust Iris Segmentation Algorithm in Non-Cooperative Environments Using Interleaved Residual U-Net"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0475-3689","authenticated-orcid":false,"given":"Yung-Hui","family":"Li","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5456-160X","authenticated-orcid":false,"given":"Wenny Ramadha","family":"Putri","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8039-2603","authenticated-orcid":false,"given":"Muhammad Saqlain","family":"Aslam","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, Taoyuan 32001, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ching-Chun","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Arsalan, M., Naqvi, R.A., Kim, D.S., Nguyen, P.H., Owais, M., and 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.","DOI":"10.3390\/s18051501"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Arsalan, M., Hong, H.G., Naqvi, R.A., Lee, M.B., Kim, M.C., Kim, D.S., Kim, C.S., and Park, K.R. (2017). Deep learning-based iris segmentation for iris recognition in visible light environment. Symmetry, 9.","DOI":"10.3390\/sym9110263"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2944","DOI":"10.1109\/TIFS.2020.2980791","article-title":"Towards Complete and Accurate Iris Segmentation Using Deep Multi-Task Attention Network for Non-Cooperative Iris Recognition","volume":"15","author":"Wang","year":"2020","journal-title":"IEEE Trans. Inf. Forensics Security"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, Y.-H., Huang, P.-J., and Juan, Y. (2019). An efficient and robust iris segmentation algorithm using deep learning. Mob. Inf. Syst., 2019.","DOI":"10.1155\/2019\/4568929"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1109\/TITB.2012.2222655","article-title":"Iris-based medical analysis by geometric deformation features","volume":"17","author":"Ma","year":"2012","journal-title":"IEEE J. Biomed. Health Informat."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"64517","DOI":"10.1109\/ACCESS.2019.2917153","article-title":"An adaptive CNNs technology for robust iris segmentation","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, Y.-H., Aslam, M.S., Yang, K.-L., Kao, C.-A., and Teng, S.-Y. (2020). Classification of Body Constitution Based on TCM Philosophy and Deep Learning. Symmetry, 12.","DOI":"10.3390\/sym12050803"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, Y.-H., and Huang, P.-J. (2017). An accurate and efficient user authentication mechanism on smart glasses based on iris recognition. Mob. Inf. Syst., 2017.","DOI":"10.1155\/2017\/1281020"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1002\/opph.201600001","article-title":"Biometric Protection for Mobile Devices is Now More Reliable: Research award for the development of an infrared LED for reliable iris recognition in smartphones and tablets","volume":"11","author":"Schnabel","year":"2016","journal-title":"Optik Photonik"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Woodard, D.L., Pundlik, S., Miller, P., Jillela, R., and Ross, A. (2010, January 23\u201326). On the fusion of periocular and iris biometrics in non-ideal imagery. Proceedings of the 20th International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.58"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1109\/34.244676","article-title":"High confidence visual recognition of persons by a test of statistical independence","volume":"15","author":"Daugman","year":"1993","journal-title":"IEEE Transact. Pattern Analysis Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1109\/5.628669","article-title":"Iris recognition: An emerging biometric technology","volume":"85","author":"Wildes","year":"1997","journal-title":"Proc. IEEE"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1049\/ip-vis:20050213","article-title":"Iris segmentation methodology for non-cooperative recognition","volume":"153","author":"Alexandre","year":"2006","journal-title":"IEE Proc. Vis. Image Signal Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.imavis.2009.05.008","article-title":"Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition","volume":"28","author":"Tan","year":"2010","journal-title":"Image Vis. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Alvarez-Betancourt, Y., and Garcia-Silvente, M. (2010, January 18\u201323). A fast iris location based on aggregating gradient approximation using QMA-OWA operator. Proceedings of the International Conference on Fuzzy Systems, Barcelona, Spain.","DOI":"10.1109\/FUZZY.2010.5584184"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1002\/int.20127","article-title":"A majority model in group decision making using QMA\u2013OWA operators","volume":"21","year":"2006","journal-title":"Int. J. Intell. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ghodrati, H., Dehghani, M.J., Helfroush, M.S., and Kazemi, K. (2010, January 7\u201310). Localization of noncircular iris boundaries using morphology and arched Hough transform. Proceedings of the 2nd International Conference on Image Processing Theory, Tools and Applications, Paris, France.","DOI":"10.1109\/IPTA.2010.5586780"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A computational approach to edge detection","volume":"PAMI-8","author":"Canny","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, X.-c., and Xiao, X.-m. (2010, January 10\u201312). An Iris segmentation method based on difference operator of radial directions. Proceedings of the 2010 Sixth International Conference on Natural Computation, Yantai, China.","DOI":"10.1109\/ICNC.2010.5583332"},{"key":"ref_20","unstructured":"Jin, L., Xiao, F., and Haopeng, W. (2010, January 29\u201330). Iris image segmentation based on K-means cluster. Proceedings of the IEEE International Conference on Intelligent Computing and Intelligent Systems, Xiamen, China."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yan, F., Tian, Y., Wu, H., Zhou, Y., Cao, L., and Zhou, C. (2014, January 9\u201311). Iris segmentation using watershed and region merging. Proceedings of the 9th IEEE Conference on Industrial Electronics and Applications, Hangzhou, China.","DOI":"10.1109\/ICIEA.2014.6931278"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"187","DOI":"10.3233\/FI-2000-411207","article-title":"The watershed transform: Definitions, algorithms and parallelization strategies","volume":"41","author":"Roerdink","year":"2000","journal-title":"Fundamenta Informaticae"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.patrec.2014.10.017","article-title":"BIRD: Watershed based iris detection for mobile devices","volume":"57","author":"Abate","year":"2015","journal-title":"Pattern Recognit. Letters"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.dsp.2017.02.003","article-title":"Automated segmentation of iris images acquired in an unconstrained environment using HOG-SVM and GrowCut","volume":"64","author":"Radman","year":"2017","journal-title":"Digital Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Banerjee, S., and Mery, D. (2015, January 23\u201327). Iris segmentation using geodesic active contours and grabcut. Proceedings of the Image and Video Technology, Auckland, New Zealand.","DOI":"10.1007\/978-3-319-30285-0_5"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Rongnian, T., and Shaojie, W. (2011, January 28\u201329). Improving iris segmentation performance via borders recognition. Proceedings of the 2011 Fourth International Conference on Intelligent Computation Technology and Automation, Shenzhen, China.","DOI":"10.1109\/ICICTA.2011.430"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, N., Li, H., Zhang, M., Liu, J., Sun, Z., and Tan, T. (2016, January 13\u201316). Accurate iris segmentation in non-cooperative environments using fully convolutional networks. Proceedings of the 2016 International Conference on Biometrics (ICB), Halmstad, Sweden.","DOI":"10.1109\/ICB.2016.7550055"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.patrec.2018.12.021","article-title":"Exploiting superior CNN-based iris segmentation for better recognition accuracy","volume":"120","author":"Hofbauer","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kerrigan, D., Trokielewicz, M., Czajka, A., and Bowyer, K.W. (2019, January 4\u20137). Iris recognition with image segmentation employing retrained off-the-shelf deep neural networks. Proceedings of the 2019 International Conference on Biometrics (ICB), Crete, Greece.","DOI":"10.1109\/ICB45273.2019.8987299"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jalilian, E., and Uhl, A. (2017). Iris segmentation using fully convolutional encoder\u2013decoder networks. Deep Learning for Biometrics, Springer.","DOI":"10.1007\/978-3-319-61657-5_6"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.jvcir.2018.10.001","article-title":"Attention guided U-Net for accurate iris segmentation","volume":"56","author":"Lian","year":"2018","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.neunet.2018.06.011","article-title":"An end to end deep neural network for iris segmentation in unconstrained scenarios","volume":"106","author":"Bazrafkan","year":"2018","journal-title":"Neural Netw."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.eswa.2019.01.010","article-title":"FRED-Net: Fully residual encoder\u2013decoder network for accurate iris segmentation","volume":"122","author":"Arsalan","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lozej, J., Meden, B., Struc, V., and Peer, P. (2018, January 18\u201320). End-to-end iris segmentation using u-net. Proceedings of the 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), San Carlos, Costa Rica.","DOI":"10.1109\/IWOBI.2018.8464213"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"123959","DOI":"10.1109\/ACCESS.2019.2938809","article-title":"Study on iris segmentation algorithm based on dense U-Net","volume":"7","author":"Wu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"85082","DOI":"10.1109\/ACCESS.2019.2924464","article-title":"A robust iris segmentation scheme based on improved U-net","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Gautam, G., and Mukhopadhyay, S. (2020). Challenges, taxonomy and techniques of iris localization: A survey. Digit. Signal Process., 102852.","DOI":"10.1016\/j.dsp.2020.102852"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, C., He, Y., Liu, Y., He, Z., He, R., and Sun, Z. (2019, January 4\u20137). Sclerasegnet: An improved u-net model with attention for accurate sclera segmentation. Proceedings of the 2019 International Conference on Biometrics (ICB), Crete, Greece.","DOI":"10.1109\/ICB45273.2019.8987270"},{"key":"ref_40","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (arXiv, 2017). Rethinking atrous convolution for semantic image segmentation, arXiv, preprint."},{"key":"ref_41","unstructured":"MacQueen, J. (July, January 21). Some methods for classification and analysis of multivariate observations. Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Berkeley, CA, USA."},{"key":"ref_42","unstructured":"CASIA (2020, August 24). Iris Image Database. Available online: http:\/\/biometrics.idealtest.org\/dbDetailForUser.do?id=4."},{"key":"ref_43","unstructured":"Kentaro Wada, K. (2020, July 10). LabelMe: Image polygonal annotation with Python. Available online: https:\/\/github.com\/wkentaro\/labelme."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European conference on computer vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE international conference on computer vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1145\/361237.361242","article-title":"Use of the Hough transformation to detect lines and curves in pictures","volume":"15","author":"Duda","year":"1972","journal-title":"Commun. ACM"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1434\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:25:57Z","timestamp":1760160357000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1434"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,18]]},"references-count":46,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21041434"],"URL":"https:\/\/doi.org\/10.3390\/s21041434","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,18]]}}}