乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:53:30Z","timestamp":1740149610242,"version":"3.37.3"},"reference-count":32,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T00:00:00Z","timestamp":1712016000000},"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":"In recent years, the advancement of generative techniques, particularly generative adversarial networks (GANs), has opened new possibilities for generating synthetic biometric data from different modalities, including\u2014among others\u2014images of irises, fingerprints, or faces in different representations. This study presents the process of generating synthetic images of human irises, using the recent StyleGAN3 model. The novelty presented in this work consists in producing generated content in both Cartesian and polar coordinate representations, typically used in iris recognition pipelines, such as the foundational work proposed by John Daugman, but hitherto not used in generative AI experiments. The main objective of this study was to conduct a qualitative analysis of the synthetic samples and evaluate the iris texture density and suitability for meaningful feature extraction. During this study, a total of 1327 unique irises were generated, and experimental results carried out using the well-known OSIRIS open-source iris recognition software and the equivalent software, wordlcoin-openiris, newly published at the end of 2023 to prove that (1) no \u201cidentity leak\u201d from the training set was observed, and (2) the generated irises had enough unique textural information to be successfully differentiated between both themselves and between them and real, authentic iris samples. The results of our research demonstrate the promising potential of synthetic iris data generation as a valuable tool for augmenting training datasets and improving the overall performance of iris recognition systems. By exploring the synthetic data in both Cartesian and polar representations, we aim to understand the benefits and limitations of each approach and their implications for biometric applications. The findings suggest that synthetic iris data can significantly contribute to the advancement of iris recognition technology, enhancing its accuracy and robustness in real-world scenarios by greatly augmenting the possibilities to gather large and diversified training datasets.<\/jats:p>","DOI":"10.3390\/s24072269","type":"journal-article","created":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T04:58:38Z","timestamp":1712120318000},"page":"2269","source":"Crossref","is-referenced-by-count":0,"title":["Synthetic Iris Images: A Comparative Analysis between Cartesian and Polar Representation"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7345-0588","authenticated-orcid":false,"given":"Adrian","family":"Kordas","sequence":"first","affiliation":[{"name":"Department of Biometrics, NASK\u2014National Research Institute, 01-045 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6245-2908","authenticated-orcid":false,"given":"Ewelina","family":"Bartuzi-Trokielewicz","sequence":"additional","affiliation":[{"name":"Department of Biometrics, NASK\u2014National Research Institute, 01-045 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1424-1245","authenticated-orcid":false,"given":"Micha\u0142","family":"O\u0142owski","sequence":"additional","affiliation":[{"name":"Department of Biometrics, NASK\u2014National Research Institute, 01-045 Warsaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7363-8385","authenticated-orcid":false,"given":"Mateusz","family":"Trokielewicz","sequence":"additional","affiliation":[{"name":"Institute of Control and Computation Engineering, Warsaw University of Technology, 00-665 Warsaw, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33887","DOI":"10.1109\/ACCESS.2023.3250852","article-title":"A Survey on Synthetic Biometrics: Fingerprint, Face, Iris and Vascular Patterns","volume":"11","author":"Makrushin","year":"2023","journal-title":"IEEE Access"},{"key":"ref_2","unstructured":"Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., and Weinberger, K. (2014). Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_3","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (arXiv, 2015). Rethinking the Inception Architecture for Computer Vision, arXiv.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_6","unstructured":"Kynk\u00e4\u00e4nniemi, T., Karras, T., Aittala, M., Aila, T., and Lehtinen, J. (2023). The Role of ImageNet Classes in Fr\u00e9chet Inception Distance. arXiv."},{"key":"ref_7","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 Transactions on Circuits and Systems for Video Technology"},{"key":"ref_8","unstructured":"Karras, T., Aittala, M., Laine, S., H\u00e4rk\u00f6nen, E., Hellsten, J., Lehtinen, J., and Aila, T. (2021). Alias-Free Generative Adversarial Networks. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.patrec.2015.09.002","article-title":"OSIRIS: An open source iris recognition software","volume":"82","author":"Othman","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_10","unstructured":"(2024, January 21). IRIS: Iris Recognition Inference System of the Worldcoin Project. Available online: https:\/\/github.com\/worldcoin\/open-iris."},{"key":"ref_11","unstructured":"Joshi, I., Grimmer, M., Rathgeb, C., Busch, C., Bremond, F., and Dantcheva, A. (2022). Synthetic Data in Human Analysis: A Survey. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kohli, N., Yadav, D., Vatsa, M., Singh, R., and Noore, A. (2017). Synthetic Iris Presentation Attack using iDCGAN. arXiv.","DOI":"10.1109\/BTAS.2017.8272756"},{"key":"ref_13","unstructured":"Minaee, S., and Abdolrashidi, A. (2018). Iris-GAN: Learning to Generate Realistic Iris Images Using Convolutional GAN. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"122134","DOI":"10.1109\/ACCESS.2019.2937809","article-title":"Conditional Generative Adversarial Network- Based Data Augmentation for Enhancement of Iris Recognition Accuracy","volume":"7","author":"Lee","year":"2019","journal-title":"IEEE Access"},{"key":"ref_15","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 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00297"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yadav, S., and Ross, A. (2020). CIT-GAN: Cyclic Image Translation Generative Adversarial Network With Application in Iris Presentation Attack Detection. arXiv.","DOI":"10.1109\/WACV48630.2021.00246"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tinsley, P., Czajka, A., and Flynn, P.J. (2022, January 10\u201313). Haven\u2019t I Seen You Before? Assessing Identity Leakage in Synthetic Irises. Proceedings of the 2022 IEEE International Joint Conference on Biometrics (IJCB), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/IJCB54206.2022.10007948"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yadav, S., and Ross, A. (2023). iWarpGAN: Disentangling Identity and Style to Generate Synthetic Iris Images. arXiv.","DOI":"10.1109\/IJCB57857.2023.10449250"},{"key":"ref_19","unstructured":"Khan, S.K., Tinsley, P., Mitcheff, M., Flynn, P., Bowyer, K.W., and Czajka, A. (2023). EyePreserve: Identity-Preserving Iris Synthesis. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"27589","DOI":"10.1007\/s11042-023-16508-1","article-title":"Segmentation-based ID preserving iris synthesis using generative adversarial networks","volume":"83","author":"Kakani","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_21","unstructured":"(2021, July 21). CASIA Iris Image Database V3.0. Chinese Academy of Sciences. Available online: http:\/\/www.cbsr.ia.ac.cn\/english\/IrisDatabase.asp."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1109\/TPAMI.2009.59","article-title":"FRVT 2006 and ICE 2006 Large-Scale Experimental Results","volume":"32","author":"Phillips","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Trokielewicz, M., Bartuzi, E., Michowska, K., Andrzejewska, A., and Selegrat, M. (2015, January 25\u201331). Exploring the feasibility of iris recognition for visible spectrum iris images obtained using smartphone camera. Proceedings of the Symposium on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (WILGA), Wilga, Poland.","DOI":"10.1117\/12.2205913"},{"key":"ref_24","unstructured":"Monro, D., Rakshit, S., and Zhang, D. (2021, July 21). UK Iris Image Database. Available online: http:\/\/www.cbsr.ia.ac.cn\/china\/Iris%20Databases%20CH.asp."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1016\/j.patcog.2006.10.014","article-title":"Biosec baseline corpus: A multimodal biometric database","volume":"40","author":"Fierrez","year":"2007","journal-title":"Pattern Recognit."},{"key":"ref_26","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_27","unstructured":"(2021). Information technology \u2013 Extensible biometric data interchange formats \u2013 Part 6: Iris image data (Standard No. ISO\/IEC 39794-6:2021)."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Melekhov, I., Kannala, J., and Rahtu, E. (2016, January 4\u20138). Siamese network features for image matching. Proceedings of the 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico.","DOI":"10.1109\/ICPR.2016.7899663"},{"key":"ref_29","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1109\/ACCESS.2017.2779107","article-title":"Feature Enhancement in Visually Impaired Images","volume":"6","author":"Suthar","year":"2018","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhou, Y., MacPhee, C., Suthar, M., and Jalali, B. (2023). PhyCV: The First Physics-inspired Computer Vision Library. arXiv.","DOI":"10.1117\/12.2649546"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1109\/TPAMI.2003.1251145","article-title":"Personal identification based on iris texture analysis","volume":"25","author":"Ma","year":"2003","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2269\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T05:10:47Z","timestamp":1712121047000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2269"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,2]]},"references-count":32,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["s24072269"],"URL":"https:\/\/doi.org\/10.3390\/s24072269","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,4,2]]}}}