{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:25:54Z","timestamp":1767338754750,"version":"build-2065373602"},"reference-count":78,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T00:00:00Z","timestamp":1646784000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Iris segmentation plays a pivotal role in the iris recognition system. The deep learning technique developed in recent years has gradually been applied to iris recognition techniques. As we all know, applying deep learning techniques requires a large number of data sets with high-quality manual labels. The larger the amount of data, the better the algorithm performs. In this paper, we propose a self-supervised framework utilizing the pix2pix conditional adversarial network for generating unlimited diversified iris images. Then, the generated iris images are used to train the iris segmentation network to achieve state-of-the-art performance. We also propose an algorithm to generate iris masks based on 11 tunable parameters, which can be generated randomly. Such a framework can generate an unlimited amount of photo-realistic training data for down-stream tasks. Experimental results demonstrate that the proposed framework achieved promising results in all commonly used metrics. The proposed framework can be easily generalized to any object segmentation task with a simple fine-tuning of the mask generation algorithm.<\/jats:p>","DOI":"10.3390\/s22062133","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T02:10:35Z","timestamp":1646878235000},"page":"2133","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Self-Supervised Learning Framework toward State-of-the-Art Iris Image Segmentation"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5456-160X","authenticated-orcid":false,"given":"Wenny Ramadha","family":"Putri","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Center University, Taoyuan 32001, Taiwan"}]},{"given":"Shen-Hsuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Center University, Taoyuan 32001, Taiwan"}]},{"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 Center University, Taoyuan 32001, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0475-3689","authenticated-orcid":false,"given":"Yung-Hui","family":"Li","sequence":"additional","affiliation":[{"name":"AI Research Center, Hon Hai Research Institute, Taipei 114699, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7319-5780","authenticated-orcid":false,"given":"Chin-Chen","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan"}]},{"given":"Jia-Ching","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Center University, Taoyuan 32001, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, Y.-H., Putri, W.R., Aslam, M.S., and Chang, C.-C.J.S. (2021). Robust Iris Segmentation Algorithm in Non-Cooperative Environments Using Interleaved Residual U-Net. Sensors, 21.","DOI":"10.3390\/s21041434"},{"key":"ref_2","unstructured":"Wang, C., Zhu, Y., Liu, Y., He, R., and Sun, Z. (2019). Joint iris segmentation and localization using deep multi-task learning framework. arXiv."},{"key":"ref_3","unstructured":"Li, Y.-H., and Savvides, M. (April, January 30). Automatic iris mask refinement for high performance iris recognition. Proceedings of the 2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications, Nashville, TN, USA."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, Y.-H., and Savvides, M. (2009). Iris Recognition, Overview. Biometrics Theory and Application, IEEE & Willey.","DOI":"10.1007\/978-0-387-73003-5_252"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhao, Z., and Kumar, A. (2017, January 22\u201329). Towards more accurate iris recognition using deeply learned spatially corresponding features. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.411"},{"key":"ref_6","first-page":"49","article-title":"Iris recognition border-crossing system in the UAE","volume":"8","author":"Daugman","year":"2004","journal-title":"Int. Airpt. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.tele.2006.06.005","article-title":"Iris recognition and the challenge of homeland and border control security in UAE","volume":"25","year":"2008","journal-title":"Telemat. Inform."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Daugman, J. (2014). 600 million citizens of India are now enrolled with biometric ID. SPIE Newsroom, 7.","DOI":"10.1117\/2.1201405.005449"},{"key":"ref_9","unstructured":"Sansola, A.J.P.D. (2015). Postmortem Iris Recognition and Its Application in Human Identification. [Master\u2019s Theses, Boston University]."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gomez-Barrero, M., Drozdowski, P., Rathgeb, C., Patino, J., Todisco, M., Nautsch, A., Damer, N., Priesnitz, J., Evans, N., and Busch, C. (2021). Biometrics in the Era of COVID-19: Challenges and Opportunities. arXiv.","DOI":"10.1109\/TTS.2022.3203571"},{"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 Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","unstructured":"Daugman, J.G. (1994). Biometric Personal Identification System Based on Iris Analysis. (5,291,560), U.S. Patent."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1023\/A:1012365806338","article-title":"Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns","volume":"45","author":"Daugman","year":"2001","journal-title":"Int. J. Comput. Vis."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Daugman, J. (2009). How iris recognition works. The Essential Guide to Image Processing, Elsevier.","DOI":"10.1016\/B978-0-12-374457-9.00025-1"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1109\/TPAMI.2012.169","article-title":"An automatic iris occlusion estimation method based on high-dimensional density estimation","volume":"35","author":"Li","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, N., Li, H., Zhang, M., Jing, L., 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_17","doi-asserted-by":"crossref","unstructured":"Jalilian, E., Uhl, A., and Kwitt, R. (2015, January 9\u201311). Domain adaptation for cnn based iris segmentation. Proceedings of the 2017 International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany.","DOI":"10.23919\/BIOSIG.2017.8053502"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Severo, E., Laroca, R., Bezerra, C.S., Zanlorensi, L.A., Weingaertner, D., Moreira, G., and Menotti, D. (2018, January 8\u201313). A benchmark for iris location and a deep learning detector evaluation. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489638"},{"key":"ref_20","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_21","first-page":"8718956","article-title":"CNN-based pupil center detection for wearable gaze estimation system","volume":"2017","author":"Chinsatit","year":"2017","journal-title":"Appl. Comput. Intell. Soft Comput."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Vera-Olmos, F.J., and Malpica, N. (2017, January 19\u201323). Deconvolutional neural network for pupil detection in real-world environments. Proceedings of the International Work-Conference on the Interplay between Natural and Artificial Computation, Corunna, Spain.","DOI":"10.1007\/978-3-319-59773-7_23"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Park, S., Zhang, X., Bulling, A., and Hilliges, O. (2018, January 14\u201317). Learning to find eye region landmarks for remote gaze estimation in unconstrained settings. Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications, Warsaw, Poland.","DOI":"10.1145\/3204493.3204545"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Parkhi, O.M., Vedaldi, A., and Zisserman, A. (2015, January 7\u201310). Deep Face Recognition. Proceedings of the British Machine Vision Conference (BMVC), Swansea, UK.","DOI":"10.5244\/C.29.41"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M.A., and Wolf, L. (2014, January 23\u201328). Deepface: Closing the gap to human-level performance in face verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Colombus, OH, USA.","DOI":"10.1109\/CVPR.2014.220"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., and Philbin, J. (2015, January 7\u201312). Facenet: A unified embedding for face recognition and clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_30","first-page":"91","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bell, S., Zitnick, C.L., Bala, K., and Girshick, R. (2016, January 27\u201330). Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.314"},{"key":"ref_32","unstructured":"(2021, October 01). Institute of Automation, Chinese Academy of Science: CASIA-Iris-Thousand Iris Image Database. Available online: http:\/\/www.cbsr.ia.ac.cn\/china\/Iris%20Databases%20CH.asp."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1109\/LRA.2019.2894849","article-title":"Learning long-range perception using self-supervision from short-range sensors and odometry","volume":"4","author":"Nava","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sayed, N., Brattoli, B., and Ommer, B. (2018, January 9\u201312). Cross and learn: Cross-modal self-supervision. Proceedings of the German Conference on Pattern Recognition, Stuttgart, Germany.","DOI":"10.1007\/978-3-030-12939-2_17"},{"key":"ref_35","unstructured":"Jang, E., Devin, C., Vanhoucke, V., and Levine, S. (2018). Grasp2vec: Learning object representations from self-supervised grasping. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Owens, A., and Efros, A.A. (2018, January 8\u201314). Audio-visual scene analysis with self-supervised multisensory features. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01231-1_39"},{"key":"ref_37","unstructured":"Zbontar, J., Jing, L., Misra, I., LeCun, Y., and Deny, S. (2021). Barlow twins: Self-supervised learning via redundancy reduction. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chen, T., Zhai, X., Ritter, M., Lucic, M., and Houlsby, N. (2019, January 15\u201320). Self-supervised gans via auxiliary rotation loss. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01243"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Huang, R., Xu, W., Lee, T.-Y., Cherian, A., Wang, Y., and Marks, T. (2020, January 1\u20135). Fx-gan: Self-supervised gan learning via feature exchange. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Snowmass, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093525"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1450","DOI":"10.1587\/transinf.2021EDP7079","article-title":"Conditional Wasserstein Generative Adversarial Networks for Rebalancing Iris Image Datasets","volume":"104","author":"Li","year":"2021","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_43","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional generative adversarial nets. arXiv."},{"key":"ref_44","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_45","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_46","doi-asserted-by":"crossref","unstructured":"Zhao, Z., and Ajay, K. (2015, January 7\u201313). An accurate iris segmentation framework under relaxed imaging constraints using total variation model. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.436"},{"key":"ref_47","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_48","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.patrec.2015.02.012","article-title":"Unsupervised detection of non-iris occlusions","volume":"57","author":"Haindl","year":"2015","journal-title":"Pattern Recognit. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Gangwar, A., Joshi, A., Singh, A., Alonso-Fernandez, F., and Bigun, J. (2016, January 13\u201316). IrisSeg: A fast and robust iris segmentation framework for non-ideal iris images. Proceedings of the 2016 International Conference on Biometrics (ICB), Halmstad, Sweden.","DOI":"10.1109\/ICB.2016.7550096"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.patrec.2014.12.012","article-title":"Improving colour iris segmentation using a model selection technique","volume":"57","author":"Hu","year":"2015","journal-title":"Pattern Recognit. Lett."},{"key":"ref_51","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_52","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":"Digit. Signal Process."},{"key":"ref_53","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_54","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_55","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_56","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_57","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_58","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_59","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_60","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_61","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_62","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_63","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., and Ronneberger, O. (2016, January 17\u201321). 3D U-Net: Learning dense volumetric segmentation from sparse annotation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Athens, Greece.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"ref_67","unstructured":"Iglovikov, V., and Shvets, A. (2018). Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"Unet++: Redesigning skip connections to exploit multiscale features in image segmentation","volume":"39","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_69","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv."},{"key":"ref_70","unstructured":"Minaee, S., and Abdolrashidi, A. (2018). Iris-GAN: Learning to generate realistic iris images using convolutional GAN. arXiv."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Zhu, J.-Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z. (2017, January 21\u201326). Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_73","unstructured":"Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., and Frey, B. (2015). Adversarial autoencoders. arXiv."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Yadav, S., Chen, C., and Ross, A. (2019, January 16\u201317). Synthesizing iris images using RaSGAN with application in presentation attack detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00297"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., and Efros, A.A. (2016, January 27\u201330). Context encoders: Feature learning by inpainting. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.278"},{"key":"ref_76","unstructured":"(2021, October 01). CASIA-Iris Database. Available online: http:\/\/www.cbsr.ia.ac.cn\/china\/Iris%20Databases%20CH.asp."},{"key":"ref_77","unstructured":"(2021, October 01). Iris Challenge Evaluation (ICE), Available online: https:\/\/www.nist.gov\/programs-projects\/iris-challenge-evaluation-ice."},{"key":"ref_78","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017). Gans trained by a two time-scale update rule converge to a local nash equilibrium. Adv. Neural Inf. Processing Syst., 30."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2133\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:33:55Z","timestamp":1760135635000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2133"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,9]]},"references-count":78,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22062133"],"URL":"https:\/\/doi.org\/10.3390\/s22062133","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,3,9]]}}}