{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T04:44:40Z","timestamp":1777092280576,"version":"3.51.4"},"reference-count":61,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T00:00:00Z","timestamp":1776384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>This study proposes a robust hybrid framework for iris segmentation in covert biometric systems, specifically addressing the challenge of non-ideal images featuring fully or nearly closed eyes. To overcome the limitations of traditional geometric methods, this study implements a SqueezeNet-based Deep Convolutional Neural Network (DCNN) for rapid eye-state classification. Comparative analysis with various pretrained DCNN models indicates that SqueezeNet provides an optimal balance of accuracy and efficiency, requiring only 1.24 million parameters and a minimal memory footprint of 5.2 MB. For iris contour demarcation, the proposed algorithm combines the Circular Hough Transform (CHT) with global gray-level statistics and anatomical constraints to facilitate reliable iris localization. Utilizing image decimation, percentile-based thresholding, and Canny edge detection, it systematically delineates the limbic and pupillary boundaries. This improved search methodology ensures precise contour delineation, even under sub-optimal imaging circumstances. The proposed algorithm was validated on a novel dataset encompassing challenging conditions such as specular reflections, blur, non-uniform illumination, and varying degrees of occlusion, including nearly or fully closed eyes. Experimental results demonstrate superior segmentation accuracy and significant computational efficiency, underscoring the model\u2019s potential for real-time biometric applications in unconstrained environments.<\/jats:p>","DOI":"10.3390\/computers15040253","type":"journal-article","created":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T14:24:51Z","timestamp":1776435891000},"page":"253","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Robust Iris Segmentation with Deep CNNs for Detecting Fully or Nearly Closed Eyes in Non-Ideal Biometric Systems"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9118-3652","authenticated-orcid":false,"given":"Farmanullah","family":"Jan","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,17]]},"reference":[{"key":"ref_1","first-page":"104680","article-title":"Deep Learning for Iris Recognition: A Survey and Analysis","volume":"130","author":"Rajaraman","year":"2024","journal-title":"Image Vis. Comput."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"102852","DOI":"10.1016\/j.dsp.2020.102852","article-title":"Challenges, taxonomy and techniques of iris localization: A survey","volume":"107","author":"Gautam","year":"2020","journal-title":"Digit. Signal Process."},{"key":"ref_4","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 Secur."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Khaki, A., Aghagolzadeh, A., and Cami, B.R. (2021, January 28\u201329). ISUR: Iris Segmentation based on UNet and ResNet. Proceedings of the 2021 11th International Conference on Computer Engineering and Knowledge (ICCKE), Mashhad, Iran.","DOI":"10.1109\/ICCKE54056.2021.9721475"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shanto, S.H., Ali, M.N., and Ahsan, S.M.M. (2022, January 24\u201326). An Advanced CNN Based Iris Recognition and Segmentation for Visible Spectrum Images. Proceedings of the 2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE), Gazipur, Bangladesh.","DOI":"10.1109\/ICAEEE54957.2022.9836333"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1109\/TBIOM.2022.3177669","article-title":"Generalized Contact Lens Iris Presentation Attack Detection","volume":"4","author":"Agarwal","year":"2022","journal-title":"IEEE Trans. Biom. Behav. Identity Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Le-Tien, T., Phan-Xuan, H., Nguyen-Duy, P., and Le-Ba, L. (2018, January 18\u201320). Iris-based Biometric Recognition using Modified Convolutional Neural Network. Proceedings of the 2018 International Conference on Advanced Technologies for Communications (ATC), Ho Chi Minh City, Vietnam.","DOI":"10.1109\/ATC.2018.8587560"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Rafik, H.D., and Boubaker, M. (2020, January 21\u201323). A Multi Biometric System Based on the Right Iris and the Left Iris Using the Combination of Convolutional Neural Networks. Proceedings of the 2020 Fourth International Conference on Intelligent Computing in Data Sciences (ICDS), Fez, Morocco.","DOI":"10.1109\/ICDS50568.2020.9268737"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Saraf, T.O.Q., Fuad, N., and Taujuddin, N.S.A.M. (2022). Feature Encoding and Selection for Iris Recognition Based on Variable Length Black Hole Optimization. Computers, 11.","DOI":"10.3390\/computers11090140"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Galterio, M.G., Shavit, S.A., and Hayajneh, T. (2018). A Review of Facial Biometrics Security for Smart Devices. Computers, 7.","DOI":"10.3390\/computers7030037"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1145\/3589958","article-title":"Awareness, Intention, (In)Action: Individuals\u2019 Reactions to Data Breaches","volume":"30","author":"Mayer","year":"2023","journal-title":"ACM Trans. Comput. Hum. Interact."},{"key":"ref_13","unstructured":"(2023, May 29). Biometric_SmartCity. Available online: https:\/\/www.smartcity.press\/cybersecurity-with-biometric-technology."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.neucom.2019.04.062","article-title":"Deepside: A General Deep Framework for Salient Object Detection","volume":"356","author":"Fu","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fu, K., Fan, D.-P., Ji, G.-P., and Zhao, Q. (2020, January 13\u201319). JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00312"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Fan, D.-P., Liu, J.-J., Gao, S., Hou, Q., Borji, A., and Cheng, M.-M. (2018, January 8\u201314). Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01267-0_12"},{"key":"ref_17","unstructured":"Wang, T., Piao, Y., Lu, H., Li, X., and Zhang, L. (November, January 27). Deep Learning for Light Field Saliency Detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, L., Zhang, J., Lin, Z., Lu, H., and He, Y. (2019, January 15\u201320). CapSal: Leveraging Captioning to Boost Semantics for Salient Object Detection. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00618"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, J., Fan, D.-P., Dai, Y., Anwar, S., Saleh, F., Zhang, T., and Barnes, N. (2020). UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders, IEEE.","DOI":"10.1109\/CVPR42600.2020.00861"},{"key":"ref_20","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 Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","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_22","unstructured":"(2023, May 29). UBIRIS_Database. Available online: https:\/\/iris.di.ubi.pt\/."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9184","DOI":"10.1016\/j.jksuci.2022.09.002","article-title":"Robust and Swift Iris Recognition at distance based on novel pupil segmentation","volume":"34","author":"Nsaif","year":"2022","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102787","DOI":"10.1016\/j.cviu.2019.07.007","article-title":"Non-ideal iris segmentation using Polar Spline RANSAC and illumination compensation","volume":"188","author":"Piuri","year":"2019","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.patcog.2017.05.021","article-title":"Long range iris recognition: A survey","volume":"72","author":"Nguyen","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_26","unstructured":"Jan, F. (2014). Development and Analysis of Robust Iris Segmentation Algorithms for Non Ideal Iris Recognition System. [Ph.D. Thesis, COMSATS Univeristy Islamabad]."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1007\/s42979-020-00344-3","article-title":"Databases for Iris Biometric Systems: A Survey","volume":"1","author":"Jan","year":"2020","journal-title":"SN Comput. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1016\/j.jksuci.2019.06.003","article-title":"Periocular biometrics: A survey","volume":"34","author":"Kumari","year":"2019","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TCSVT.2003.818350","article-title":"How Iris Recognition Works","volume":"14","author":"Daugman","year":"2004","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kim, K.W., Hong, H.G., Nam, G.P., and Park, K.R. (2017). A Study of Deep CNN-Based Classification of Open and Closed Eyes Using a Visible Light Camera Sensor. Sensors, 17.","DOI":"10.3390\/s17071534"},{"key":"ref_31","first-page":"1970","article-title":"Open and Closed Eyes Classification in Different Lighting Conditions Using New Convolution Neural Networks Architecture","volume":"97","year":"2019","journal-title":"J. Theor. Appl. Inf. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/MCE.2019.2892286","article-title":"Combining Unmanned Aerial Vehicles With Artificial-Intelligence Technology for Traffic-Congestion Recognition: Electronic Eyes in the Skies to Spot Clogged Roads","volume":"8","author":"Jian","year":"2019","journal-title":"IEEE Consum. Electron. Mag."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"108301","DOI":"10.1016\/j.jneumeth.2019.05.016","article-title":"DeepVOG: Open-source pupil segmentation and gaze estimation in neuroscience using deep learning","volume":"324","author":"Yiu","year":"2019","journal-title":"J. Neurosci. Methods"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s42979-019-0001-4","article-title":"An End-to-End Recognition System for Unconstrained Vietnamese Handwriting","volume":"1","author":"Le","year":"2019","journal-title":"SN Comput. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hajjami, A., Khalid, A., and Arsalane, Z. (2019, January 28\u201330). Iris Localisation and segmentation using Convolutional neural network. Proceedings of the 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS), Marrakech, Morocco.","DOI":"10.1109\/ICDS47004.2019.8942341"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1049\/iet-bmt.2018.5146","article-title":"Iris super-resolution using CNNs: Is photo-realism important to iris recognition?","volume":"8","author":"Ribeiro","year":"2019","journal-title":"IET Biom."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1007\/s10044-017-0656-1","article-title":"A multi-biometric iris recognition system based on a deep learning approach","volume":"21","author":"Qahwaji","year":"2018","journal-title":"Pattern Anal. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"964","DOI":"10.1109\/TPAMI.2008.185","article-title":"The Best Bits in an Iris Code","volume":"31","author":"Hollingsworth","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.cviu.2007.08.005","article-title":"Image Understanding for Iris Biometrics: A Survey","volume":"110","author":"Bowyer","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_40","unstructured":"(2011). Information Technology\u2014Biometric Data Interchange Formats\u2014Part 6: Iris Image Data (Standard No. ISO\/IEC 19794-6)."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.imavis.2009.03.003","article-title":"Iris Recognition: Analysis of the Error Rates Regarding the Accuracy of the Segmentation Stage","volume":"28","author":"Alexandre","year":"2010","journal-title":"Image Vis. Comput."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Proen\u00e7a, H., and Alexandre, L.A. (2005, January 6\u20138). UBIRIS: A Noisy Iris Image Database. Proceedings of the International Conference on Image Analysis and Processing (ICIAP), Cagliari, Italy.","DOI":"10.1007\/11553595_119"},{"key":"ref_43","first-page":"4568929","article-title":"An Efficient and Robust Iris Segmentation Algorithm Using Deep Learning","volume":"2019","author":"Li","year":"2019","journal-title":"Mob. Inf. Syst."},{"key":"ref_44","unstructured":"(2023, May 29). ImageNet. Available online: https:\/\/www.image-net.org\/."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1177\/14759217241293467","article-title":"Cycle-consistency-constrained few-shot learning framework for universal multi-type structural damage segmentation","volume":"25","author":"Fan","year":"2026","journal-title":"Struct. Health Monit."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"106256","DOI":"10.1016\/j.autcon.2025.106256","article-title":"Transformer-based large vision model for universal structural damage segmentation","volume":"176","author":"Xu","year":"2025","journal-title":"Autom. Constr."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"20240032","DOI":"10.1002\/VIW.20240032","article-title":"Deep learning-enhanced microwell array biochip for rapid and precise quantification of Cryptococcus subtypes","volume":"5","author":"Tong","year":"2024","journal-title":"View"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"20250157","DOI":"10.1002\/VIW.20250157","article-title":"A multimodal deep learning framework for predicting sunitinib response in advanced clear cell renal cell carcinoma","volume":"7","author":"Tian","year":"2025","journal-title":"View"},{"key":"ref_49","unstructured":"(2023, May 29). Mathworks. Available online: http:\/\/www.mathworks.com\/."},{"key":"ref_50","first-page":"2069","article-title":"Deployment of real-time systems in the cloud environment","volume":"77","author":"Qureshi","year":"2020","journal-title":"J. Supercomput."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1016\/j.bbe.2020.06.002","article-title":"An effective iris segmentation scheme for noisy images","volume":"40","author":"Jan","year":"2020","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_52","unstructured":"(2023, May 29). MMU_Iris_Database. Available online: https:\/\/www.kaggle.com\/datasets\/naureenmohammad\/mmu-iris-dataset."},{"key":"ref_53","unstructured":"(2023, May 29). IITD_Iris_Databases. Available online: https:\/\/www4.comp.polyu.edu.hk\/~csajaykr\/IITD\/Database_Iris.htm."},{"key":"ref_54","unstructured":"(2026, April 14). CASIA_Database. Available online: https:\/\/www.kaggle.com\/swoyam2609\/datasets."},{"key":"ref_55","unstructured":"(2026, April 14). SGGSIE&T_iris_database. Available online: https:\/\/sggs.ac.in\/home\/page\/electronics-and-telecommunication-engineeringl."},{"key":"ref_56","unstructured":"CEW_datset (2023, May 29). Closed Eyes in the Wild (CEW). Available online: http:\/\/parnec.nuaa.edu.cn\/_upload\/tpl\/02\/db\/731\/template731\/pages\/xtan\/ClosedEyeDatabases.html."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1016\/j.dsp.2012.06.001","article-title":"Iris localization in frontal eye images for less constrained iris recognition systems","volume":"22","author":"Jan","year":"2012","journal-title":"Digit. Signal Process."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"15223","DOI":"10.1007\/s11042-021-11075-9","article-title":"Iris segmentation for non-ideal Iris biometric systems","volume":"83","author":"Jan","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1016\/j.optlaseng.2011.11.008","article-title":"Iris localization using local histogram and other image statistics","volume":"50","author":"Ibrahim","year":"2012","journal-title":"Opt. Lasers Eng."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.optlaseng.2010.08.020","article-title":"Automatic localization of pupil using eccentricity and iris using gradient based method","volume":"49","author":"Khan","year":"2011","journal-title":"Opt. Lasers Eng."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1109\/TSMCB.2007.903540","article-title":"New Methods in Iris Recognition","volume":"37","author":"Daugman","year":"2007","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/15\/4\/253\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T04:16:21Z","timestamp":1777090581000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/15\/4\/253"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,17]]},"references-count":61,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["computers15040253"],"URL":"https:\/\/doi.org\/10.3390\/computers15040253","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,17]]}}}