{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T16:22:34Z","timestamp":1772814154522,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T00:00:00Z","timestamp":1637884800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T00:00:00Z","timestamp":1637884800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["ATHENE"],"award-info":[{"award-number":["ATHENE"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003495","name":"Hessisches Ministerium f\u00fcr Wissenschaft und Kunst","doi-asserted-by":"crossref","award":["ATHENE"],"award-info":[{"award-number":["ATHENE"]}],"id":[{"id":"10.13039\/501100003495","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Iris Presentation Attack Detection (PAD) algorithms address the vulnerability of iris recognition systems to presentation attacks. With the great success of deep learning methods in various computer vision fields, neural network-based iris PAD algorithms emerged. However, most PAD networks suffer from overfitting due to insufficient iris data variability. Therefore, we explore the impact of various data augmentation techniques on performance and the generalizability of iris PAD. We apply several data augmentation methods to generate variability, such as shift, rotation, and brightness. We provide in-depth analyses of the overlapping effect of these methods on performance. In addition to these widely used augmentation techniques, we also propose an augmentation selection protocol based on the assumption that various augmentation techniques contribute differently to the PAD performance. Moreover, two fusion methods are performed for more comparisons: the strategy-level and the score-level combination. We demonstrate experiments on two fine-tuned models and one trained from the scratch network and perform on the datasets in the Iris-LivDet-2017 competition designed for generalizability evaluation. Our experimental results show that augmentation methods improve iris PAD performance in many cases. Our least overlap-based augmentation selection protocol achieves the lower error rates for two networks. Besides, the shift augmentation strategy also exceeds state-of-the-art (SoTA) algorithms on the Clarkson and IIITD-WVU datasets.<\/jats:p>","DOI":"10.1007\/s00138-021-01256-9","type":"journal-article","created":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T17:02:41Z","timestamp":1637946161000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["The overlapping effect and fusion protocols of data augmentation techniques in iris PAD"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3058-2553","authenticated-orcid":false,"given":"Meiling","family":"Fang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naser","family":"Damer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fadi","family":"Boutros","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Florian","family":"Kirchbuchner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arjan","family":"Kuijper","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,11,26]]},"reference":[{"key":"1256_CR1","first-page":"62","volume-title":"In: 2nd International Conference Document Analysis and Recognition","author":"HS Baird","year":"1993","unstructured":"Baird, H.S.: Document image defect models and their uses. In: In: 2nd International Conference Document Analysis and Recognition, pp. 62\u201367. IEEE Computer Society, Tsukuba City, Japan (1993)"},{"issue":"2","key":"1256_CR2","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1504\/IJBM.2013.052965","volume":"5","author":"S Bakshi","year":"2013","unstructured":"Bakshi, S., Mehrotra, H., Majhi, B.: Postmatch pruning of SIFT pairs for iris recognition. Int. J. Biom. 5(2), 160\u2013180 (2013). https:\/\/doi.org\/10.1504\/IJBM.2013.052965","journal-title":"Int. J. Biom."},{"issue":"6","key":"1256_CR3","doi-asserted-by":"publisher","first-page":"7637","DOI":"10.1007\/s11042-017-4668-z","volume":"77","author":"SS Barpanda","year":"2018","unstructured":"Barpanda, S.S., Sa, P.K., Marques, O., Majhi, B., Bakshi, S.: Iris recognition with tunable filter bank based feature. Multim. Tools Appl. 77(6), 7637\u20137674 (2018). https:\/\/doi.org\/10.1007\/s11042-017-4668-z","journal-title":"Multim. Tools Appl."},{"key":"1256_CR4","doi-asserted-by":"crossref","unstructured":"Boutros, F., Damer, N., Raja, K.B., Ramachandra, R., Kirchbuchner, F., Kuijper, A.: Iris and periocular biometrics for head mounted displays: Segmentation, recognition, and synthetic data generation. Image Vis. Comput. 104, 104007 (2020)","DOI":"10.1016\/j.imavis.2020.104007"},{"key":"1256_CR5","doi-asserted-by":"crossref","unstructured":"Boutros, F., Damer, N., Raja, K.B., Ramachandra, R., Kirchbuchner, F., Kuijper, A.: In: IJCB, (ed.) On benchmarking iris recognition within a head-mounted display for AR\/VR applications, pp. 1\u201310. IEEE (2020)","DOI":"10.1109\/IJCB48548.2020.9304919"},{"key":"1256_CR6","doi-asserted-by":"publisher","unstructured":"Chen, C., Ross, A.: A multi-task convolutional neural network for joint iris detection and presentation attack detection. In: 2018 IEEE Winter Applications of Computer Vision Workshops, WACV Workshops 2018, Lake Tahoe, NV, USA, March 15, 2018, pp. 44\u201351. IEEE Computer Society (2018). https:\/\/doi.org\/10.1109\/WACVW.2018.00011","DOI":"10.1109\/WACVW.2018.00011"},{"key":"1256_CR7","doi-asserted-by":"crossref","unstructured":"Choudhary, M., Tiwari, V., Uduthalapally, V.: Iris presentation attack detection based on best-k feature selection from yolo inspired roi. In: Neural Comput and Applic (2020)","DOI":"10.1007\/s00521-020-05342-3"},{"issue":"4","key":"1256_CR8","doi-asserted-by":"publisher","first-page":"86:1\u201386:35","DOI":"10.1145\/3232849","volume":"51","author":"A Czajka","year":"2018","unstructured":"Czajka, A., Bowyer, K.W.: Presentation attack detection for iris recognition: An assessment of the state-of-the-art. ACM Comput. Surv. 51(4), 86:1-86:35 (2018). https:\/\/doi.org\/10.1145\/3232849","journal-title":"ACM Comput. Surv."},{"key":"1256_CR9","unstructured":"Damer, N., Opel, A., Nouak, A.: Biometric source weighting in multi-biometric fusion: Towards a generalized and robust solution. In: 22nd European Signal Processing Conference, EUSIPCO 2014, Lisbon, Portugal, September 1-5, 2014, pp. 1382\u20131386. IEEE (2014)"},{"key":"1256_CR10","unstructured":"Dao, T., Gu, A., Ratner, A., Smith, V., Sa, C.D., R\u00e9, C.: A kernel theory of modern data augmentation. In: K.\u00a0Chaudhuri, R.\u00a0Salakhutdinov (eds.) Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, Proceedings of Machine Learning Research, vol.\u00a097, pp. 1528\u20131537. PMLR (2019)"},{"key":"1256_CR11","doi-asserted-by":"publisher","unstructured":"Das, P., McGrath, J., Fang, Z., Boyd, A., Jang, G., Mohammadi, A., Purnapatra, S., Yambay, D., Marcel, S., Trokielewicz, M., Maciejewicz, P., Bowyer, K.W., Czajka, A., Schuckers, S., Tapia, J.E., Gonzalez, S., Fang, M., Damer, N., Boutros, F., Kuijper, A., Sharma, R., Chen, C., Ross, A.: Iris liveness detection competition (livdet-iris) - the 2020 edition. In: 2020 IEEE International Joint Conference on Biometrics, IJCB 2020, Houston, TX, USA, September 28 - October 1, 2020, pp. 1\u20139. IEEE (2020).https:\/\/doi.org\/10.1109\/IJCB48548.2020.9304941","DOI":"10.1109\/IJCB48548.2020.9304941"},{"key":"1256_CR12","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20-25 June 2009, Miami, Florida, USA, pp. 248\u2013255. IEEE Computer Society (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1256_CR13","doi-asserted-by":"publisher","unstructured":"Fang, M., Damer, N., Boutros, F., Kirchbuchner, F., Kuijper, A.: Deep learning multi-layer fusion for an accurate iris presentation attack detection. In: IEEE 23rd International Conference on Information Fusion, FUSION 2020, Rustenburg, South Africa, July 6-9, 2020, pp. 1\u20138. IEEE (2020). https:\/\/doi.org\/10.23919\/FUSION45008.2020.9190424","DOI":"10.23919\/FUSION45008.2020.9190424"},{"key":"1256_CR14","doi-asserted-by":"publisher","unstructured":"Fang, M., Damer, N., Boutros, F., Kirchbuchner, F., Kuijper, A.: Cross-database and cross-attack iris presentation attack detection using micro stripes analyses. Image Vis. Comput. 105, 104057 (2021). https:\/\/doi.org\/10.1016\/j.imavis.2020.104057","DOI":"10.1016\/j.imavis.2020.104057"},{"key":"1256_CR15","doi-asserted-by":"crossref","unstructured":"Fang, M., Damer, N., Boutros, F., Kirchbuchner, F., Kuijper, A.: Iris presentation attack detection by attention-based and deep pixel-wise binary supervision network. In: 2021 IEEE International Joint Conference on Biometrics, IJCB 2021, Shenzhen, China, Aug.4 - 7, 2021. IEEE (2021)","DOI":"10.1109\/IJCB52358.2021.9484343"},{"key":"1256_CR16","doi-asserted-by":"publisher","unstructured":"Fang, M., Damer, N., Kirchbuchner, F., Kuijper, A.: Demographic bias in presentation attack detection of iris recognition systems. In: 28th European Signal Processing Conference, EUSIPCO 2020, Amsterdam, Netherlands, January 18-21, 2021, pp. 835\u2013839. IEEE (2020). https:\/\/doi.org\/10.23919\/Eusipco47968.2020.9287321","DOI":"10.23919\/Eusipco47968.2020.9287321"},{"key":"1256_CR17","doi-asserted-by":"publisher","unstructured":"Fang, M., Damer, N., Kirchbuchner, F., Kuijper, A.: Micro stripes analyses for iris presentation attack detection. In: 2020 IEEE International Joint Conference on Biometrics, IJCB 2020, Houston, TX, USA, September 28 - October 1, 2020, pp. 1\u201310. IEEE (2020). https:\/\/doi.org\/10.1109\/IJCB48548.2020.9304886","DOI":"10.1109\/IJCB48548.2020.9304886"},{"key":"1256_CR18","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Z.\u00a0Ghahramani, M.\u00a0Welling, C.\u00a0Cortes, N.D. Lawrence, K.Q. Weinberger (eds.) Advances in Neural Information Processing Systems 27, pp. 2672\u20132680. Curran Associates, Inc. (2014). http:\/\/papers.nips.cc\/paper\/5423-generative-adversarial-nets.pdf"},{"key":"1256_CR19","doi-asserted-by":"publisher","unstructured":"Gragnaniello, D., Sansone, C., Poggi, G., Verdoliva, L.: Biometric spoofing detection by a domain-aware convolutional neural network. In: 2016 12th International Conference on Signal-Image Technology Internet-Based Systems (SITIS), pp. 193\u2013198 (2016). https:\/\/doi.org\/10.1109\/SITIS.2016.38","DOI":"10.1109\/SITIS.2016.38"},{"key":"1256_CR20","doi-asserted-by":"publisher","unstructured":"Gupta, P., Behera, S., Vatsa, M., Singh, R.: On iris spoofing using print attack. In: 22nd International Conference on Pattern Recognition, ICPR 2014, Stockholm, Sweden, August 24-28, 2014, pp. 1681\u20131686. IEEE Computer Society (2014). https:\/\/doi.org\/10.1109\/ICPR.2014.296","DOI":"10.1109\/ICPR.2014.296"},{"key":"1256_CR21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE CVPR, Las Vegas, NV, USA, June 27-30, 2016, pp. 770\u2013778. IEEE Computer Society (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1256_CR22","doi-asserted-by":"crossref","unstructured":"Howard, A., Pang, R., Adam, H., Le, Q.V., Sandler, M., Chen, B., Wang, W., Chen, L., Tan, M., Chu, G., Vasudevan, V., Zhu, Y.: Searching for mobilenetv3. In: 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, pp. 1314\u20131324. IEEE (2019)","DOI":"10.1109\/ICCV.2019.00140"},{"key":"1256_CR23","unstructured":"Hu, B., Lei, C., Wang, D., Zhang, S., Chen, Z.: A preliminary study on data augmentation of deep learning for image classification. CoRR arXiv:1906.11887 (2019)"},{"key":"1256_CR24","unstructured":"International Organization for Standardization: ISO\/IEC DIS 30107-3:2016: Information Technology \u2013 Biometric presentation attack detection \u2013 P. 3: Testing and reporting (2017)"},{"key":"1256_CR25","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: F.\u00a0Bach, D.\u00a0Blei (eds.) Proceedings of the 32nd International Conference on Machine Learning, Proceedings of Machine Learning Research, vol.\u00a037, pp. 448\u2013456. PMLR, Lille, France (2015)"},{"key":"1256_CR26","doi-asserted-by":"publisher","unstructured":"Kohli, N., Yadav, D., Vatsa, M., Singh, R., Noore, A.: Synthetic iris presentation attack using idcgan. In: 2017 IEEE International Joint Conference on Biometrics, IJCB 2017, Denver, CO, USA, October 1-4, 2017, pp. 674\u2013680. IEEE (2017). https:\/\/doi.org\/10.1109\/BTAS.2017.8272756","DOI":"10.1109\/BTAS.2017.8272756"},{"issue":"6","key":"1256_CR27","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1109\/TIFS.2018.2878542","volume":"14","author":"A Kuehlkamp","year":"2019","unstructured":"Kuehlkamp, A., Pinto, A., Rocha, A., Bowyer, K.W., Czajka, A.: Ensemble of multi-view learning classifiers for cross-domain iris presentation attack detection. IEEE Transactions on Information Forensics and Security 14(6), 1419\u20131431 (2019)","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"1256_CR28","doi-asserted-by":"publisher","unstructured":"Lorena, A.C., de Leon Ferreira de Carvalho, A.C.P.: Building binary-tree-based multiclass classifiers using separability measures. Neurocomputing 73(16\u201318), 2837\u20132845 (2010). https:\/\/doi.org\/10.1016\/j.neucom.2010.03.027","DOI":"10.1016\/j.neucom.2010.03.027"},{"issue":"8","key":"1256_CR29","doi-asserted-by":"publisher","first-page":"2601","DOI":"10.3390\/s18082601","volume":"18","author":"DT Nguyen","year":"2018","unstructured":"Nguyen, D.T., Pham, T.D., Lee, Y., Park, K.R.: Deep learning-based enhanced presentation attack detection for iris recognition by combining features from local and global regions based on NIR camera sensor. Sensors 18(8), 2601 (2018)","journal-title":"Sensors"},{"key":"1256_CR30","doi-asserted-by":"publisher","unstructured":"Raghavendra, R., Raja, K.B., Busch, C.: Contlensnet: Robust iris contact lens detection using deep convolutional neural networks. In: 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017, Santa Rosa, CA, USA, March 24-31, 2017, pp. 1160\u20131167. IEEE Computer Society (2017). https:\/\/doi.org\/10.1109\/WACV.2017.134","DOI":"10.1109\/WACV.2017.134"},{"key":"1256_CR31","doi-asserted-by":"crossref","unstructured":"Sharma, R., Ross, A.: D-netpad: An explainable and interpretable iris presentation attack detector. 2020 IJCB, Sep. 28 - Oct. 1, 2020, online conference arXiv: 2007.01381 (2020)","DOI":"10.1109\/IJCB48548.2020.9304880"},{"key":"1256_CR32","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6, 60 (2019). https:\/\/doi.org\/10.1186\/s40537-019-0197-0","journal-title":"J. Big Data"},{"key":"1256_CR33","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Y.\u00a0Bengio, Y.\u00a0LeCun (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015)"},{"issue":"56","key":"1256_CR34","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15(56), 1929\u20131958 (2014)","journal-title":"Journal of Machine Learning Research"},{"key":"1256_CR35","doi-asserted-by":"publisher","first-page":"18848","DOI":"10.1109\/ACCESS.2017.2784352","volume":"6","author":"KN Thanh","year":"2018","unstructured":"Thanh, K.N., Fookes, C., Ross, A., Sridharan, S.: Iris recognition with off-the-shelf CNN features: A deep learning perspective. IEEE Access 6, 18848\u201318855 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2017.2784352","journal-title":"IEEE Access"},{"key":"1256_CR36","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1109\/TIFS.2019.2934867","volume":"15","author":"R Tolosana","year":"2020","unstructured":"Tolosana, R., Gomez-Barrero, M., Busch, C., Ortega-Garcia, J.: Biometric presentation attack detection: Beyond the visible spectrum. IEEE Trans. Information Forensics and Security 15, 1261\u20131275 (2020)","journal-title":"IEEE Trans. Information Forensics and Security"},{"key":"1256_CR37","doi-asserted-by":"publisher","unstructured":"Wei, Z., Tan, T., Sun, Z.: Synthesis of large realistic iris databases using patch-based sampling. In: 19th International Conference on Pattern Recognition (ICPR 2008), December 8-11, 2008, Tampa, Florida, USA, pp. 1\u20134. IEEE Computer Society (2008). https:\/\/doi.org\/10.1109\/ICPR.2008.4761674","DOI":"10.1109\/ICPR.2008.4761674"},{"key":"1256_CR38","doi-asserted-by":"crossref","unstructured":"Wong, S.C., Gatt, A., Stamatescu, V., McDonnell, M.D.: Understanding data augmentation for classification: When to warp? In: 2016 International Conference on DICTA, 2016, Gold Coast, Australia, November 30 - December 2, 2016, pp. 1\u20136. IEEE (2016)","DOI":"10.1109\/DICTA.2016.7797091"},{"key":"1256_CR39","doi-asserted-by":"publisher","unstructured":"Yadav, D., Kohli, N., Agarwal, A., Vatsa, M., Singh, R., Noore, A.: Fusion of handcrafted and deep learning features for large-scale multiple iris presentation attack detection. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2018, Salt Lake City, UT, USA, June 18-22, 2018, pp. 572\u2013579. IEEE Computer Society (2018). https:\/\/doi.org\/10.1109\/CVPRW.2018.00099","DOI":"10.1109\/CVPRW.2018.00099"},{"key":"1256_CR40","doi-asserted-by":"publisher","unstructured":"Yadav, D., Kohli, N., Jr., J.S.D., Singh, R., Vatsa, M., Bowyer, K.W.: Unraveling the effect of textured contact lenses on iris recognition. IEEE Trans. Inf. Forensics Secur. 9(5), 851\u2013862 (2014). https:\/\/doi.org\/10.1109\/TIFS.2014.2313025","DOI":"10.1109\/TIFS.2014.2313025"},{"key":"1256_CR41","doi-asserted-by":"publisher","unstructured":"Yadav, S., Chen, C., Ross, A.: Synthesizing iris images using rasgan with application in presentation attack detection. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2019, Long Beach, CA, USA, June 16-20, 2019, pp. 2422\u20132430. Computer Vision Foundation \/ IEEE (2019). https:\/\/doi.org\/10.1109\/CVPRW.2019.00297","DOI":"10.1109\/CVPRW.2019.00297"},{"key":"1256_CR42","doi-asserted-by":"crossref","unstructured":"Yambay, D., Becker, B., Kohli, N., Yadav, D., Czajka, A., Bowyer, K.W., Schuckers, S., Singh, R., Vatsa, M., Noore, A., Gragnaniello, D., Sansone, C., Verdoliva, L., He, L., Ru, Y., Li, H., Liu, N., Sun, Z., Tan, T.: Livdet iris 2017 - iris liveness detection competition 2017. In: 2017 IEEE IJCB, Denver, CO, USA, October 1-4, 2017, pp. 733\u2013741. IEEE (2017)","DOI":"10.1109\/BTAS.2017.8272763"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-021-01256-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-021-01256-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-021-01256-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,4]],"date-time":"2022-02-04T16:50:28Z","timestamp":1643993428000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-021-01256-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,26]]},"references-count":42,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["1256"],"URL":"https:\/\/doi.org\/10.1007\/s00138-021-01256-9","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,26]]},"assertion":[{"value":"26 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 September 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 October 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"8"}}