{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T11:04:43Z","timestamp":1773486283455,"version":"3.50.1"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100008963","name":"Guangdong University of Petrochemical Technology","doi-asserted-by":"publisher","award":["702\/519245"],"award-info":[{"award-number":["702\/519245"]}],"id":[{"id":"10.13039\/100008963","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s10044-026-01619-w","type":"journal-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:21:21Z","timestamp":1769718081000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Interactive feature learning framework for mask-occluded face recognition"],"prefix":"10.1007","volume":"29","author":[{"given":"M. Saad","family":"Shakeel","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"key":"1619_CR1","doi-asserted-by":"crossref","unstructured":"Wang H, Wang Y, Zhou Z, Ji X, Gong D, Zhou J, Li Z, Liu W (2018) Cosface: large margin cosine loss for deep face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5265\u20135274","DOI":"10.1109\/CVPR.2018.00552"},{"key":"1619_CR2","doi-asserted-by":"crossref","unstructured":"Deng J, Guo J, Xue N, Zafeiriou S (2019) Arcface: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 4690\u20134699","DOI":"10.1109\/CVPR.2019.00482"},{"key":"1619_CR3","doi-asserted-by":"crossref","unstructured":"Huang Y, Wang Y, Tai Y, Liu X, Shen P, Li S, Li J, Huang F (2020) Curricularface: adaptive curriculum learning loss for deep face recognition. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5901\u20135910","DOI":"10.1109\/CVPR42600.2020.00594"},{"key":"1619_CR4","doi-asserted-by":"crossref","unstructured":"Meng Q, Zhao S, Huang Z, Zhou F (2021) Magface: a universal representation for face recognition and quality assessment. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 14225\u201314234","DOI":"10.1109\/CVPR46437.2021.01400"},{"issue":"2","key":"1619_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10044-024-01255-2","volume":"27","author":"Y Liu","year":"2024","unstructured":"Liu Y, Chen J, Li Y, Wu T, Wen H (2024) Joint face normalization and representation learning for face recognition. Pattern Anal Appl 27(2):1\u201315","journal-title":"Pattern Anal Appl"},{"key":"1619_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108308","volume":"122","author":"G Jeevan","year":"2022","unstructured":"Jeevan G, Zacharias GC, Nair MS, Rajan J (2022) An empirical study of the impact of masks on face recognition. Pattern Recogn 122:108308","journal-title":"Pattern Recogn"},{"key":"1619_CR7","unstructured":"Anwar A, Raychowdhury A (2020) Masked face recognition for secure authentication. arXiv preprint arXiv:2008.11104"},{"key":"1619_CR8","doi-asserted-by":"crossref","unstructured":"Wang J, Liu Y, Hu Y, Shi H, Mei T (2021) Facex-zoo: A pytorch toolbox for face recognition. In: Proceedings of the 29th ACM international conference on multimedia, pp 3779\u20133782","DOI":"10.1145\/3474085.3478324"},{"issue":"11","key":"1619_CR9","first-page":"2579","volume":"9","author":"L Maaten","year":"2008","unstructured":"Maaten L, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9(11):2579\u20132605","journal-title":"J Mach Learn Res"},{"key":"1619_CR10","doi-asserted-by":"crossref","unstructured":"Cao Q, Shen L, Xie W, Parkhi OM, Zisserman A (2018) Vggface2: a dataset for recognising faces across pose and age. In: 2018 13th IEEE international conference on automatic face & gesture recognition (FG 2018), pp 67\u201374 . IEEE","DOI":"10.1109\/FG.2018.00020"},{"key":"1619_CR11","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee JY, Kweon IS (2018) Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"1619_CR12","doi-asserted-by":"crossref","unstructured":"Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) Eca-net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11534\u201311542","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"1619_CR13","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"3","key":"1619_CR14","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/s10044-024-01307-7","volume":"27","author":"S Liu","year":"2024","unstructured":"Liu S, Wang P, Lin Y, Zhou B (2024) Smru-net: skin disease image segmentation using channel-space separate attention with depthwise separable convolutions. Pattern Anal Appl 27(3):93","journal-title":"Pattern Anal Appl"},{"key":"1619_CR15","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3146\u20133154","DOI":"10.1109\/CVPR.2019.00326"},{"issue":"3","key":"1619_CR16","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1007\/s10044-024-01317-5","volume":"27","author":"Q Li","year":"2024","unstructured":"Li Q, Wu W (2024) Attention-based supervised contrastive learning on fine-grained image classification. Pattern Anal Appl 27(3):96","journal-title":"Pattern Anal Appl"},{"issue":"3","key":"1619_CR17","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1007\/s10044-024-01290-z","volume":"27","author":"H Deng","year":"2024","unstructured":"Deng H, Wang C, Li C, Hao Z (2024) Fine grained dual level attention mechanisms with spacial context information fusion for object detection. Pattern Anal Appl 27(3):75","journal-title":"Pattern Anal Appl"},{"key":"1619_CR18","doi-asserted-by":"crossref","unstructured":"Ding F, Peng P, Huang Y, Geng M, Tian Y (2020) Masked face recognition with latent part detection. In: Proceedings of the 28th ACM international conference on multimedia, pp 2281\u20132289","DOI":"10.1145\/3394171.3413731"},{"key":"1619_CR19","doi-asserted-by":"publisher","first-page":"3012","DOI":"10.1007\/s10489-020-02100-9","volume":"51","author":"Y Li","year":"2021","unstructured":"Li Y, Guo K, Lu Y, Liu L (2021) Cropping and attention based approach for masked face recognition. Appl Intell 51:3012\u20133025","journal-title":"Appl Intell"},{"key":"1619_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108522","volume":"126","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Wang X, Shakeel MS, Wan H, Kang W (2022) Learning upper patch attention using dual-branch training strategy for masked face recognition. Pattern Recogn 126:108522","journal-title":"Pattern Recogn"},{"key":"1619_CR21","doi-asserted-by":"publisher","first-page":"496","DOI":"10.1016\/j.neucom.2022.10.025","volume":"518","author":"Y Ge","year":"2023","unstructured":"Ge Y, Liu H, Du J, Li Z, Wei Y (2023) Masked face recognition with convolutional visual self-attention network. Neurocomputing 518:496\u2013506","journal-title":"Neurocomputing"},{"key":"1619_CR22","doi-asserted-by":"crossref","unstructured":"Zhang H, Wang W, Deng J, Guo Y, Liu S, Zhang J (2025) Masff-net: multi-azimuth scattering feature fusion network for sar target recognition. IEEE J Sel Top Appl Earth Observ Remote Sens","DOI":"10.1109\/JSTARS.2025.3591795"},{"key":"1619_CR23","doi-asserted-by":"crossref","unstructured":"Sun Z, Leng X, Zhang X, Zhou Z, Xiong B, Ji K, Kuang G (2025) Arbitrary-direction sar ship detection method for multi-scale imbalance. IEEE Trans Geosci Remote Sens","DOI":"10.1109\/TGRS.2025.3559701"},{"key":"1619_CR24","doi-asserted-by":"crossref","unstructured":"Song L, Gong D, Li Z, Liu C, Liu W (2019) Occlusion robust face recognition based on mask learning with pairwise differential siamese network. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 773\u2013782","DOI":"10.1109\/ICCV.2019.00086"},{"key":"1619_CR25","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30"},{"key":"1619_CR26","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.imavis.2018.09.011","volume":"79","author":"DS Trigueros","year":"2018","unstructured":"Trigueros DS, Meng L, Hartnett M (2018) Enhancing convolutional neural networks for face recognition with occlusion maps and batch triplet loss. Image Vis Comput 79:99\u2013108","journal-title":"Image Vis Comput"},{"key":"1619_CR27","doi-asserted-by":"crossref","unstructured":"Liu Y, Luo G, Weng Z, Zhu Y (2024) Adaptive face recognition for multi-type occlusions. IEEE Trans Circ Syst Video Technol","DOI":"10.1109\/TCSVT.2024.3419933"},{"issue":"3","key":"1619_CR28","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1109\/TIFS.2017.2763119","volume":"13","author":"S Zhang","year":"2017","unstructured":"Zhang S, He R, Sun Z, Tan T (2017) Demeshnet: blind face inpainting for deep meshface verification. IEEE Trans Inf Forensics Secur 13(3):637\u2013647","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"10","key":"1619_CR29","doi-asserted-by":"publisher","first-page":"2380","DOI":"10.1109\/TPAMI.2018.2858819","volume":"41","author":"J Zhao","year":"2018","unstructured":"Zhao J, Xiong L, Li J, Xing J, Yan S, Feng J (2018) 3d-aided dual-agent gans for unconstrained face recognition. IEEE Trans Pattern Anal Mach Intell 41(10):2380\u20132394","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"10","key":"1619_CR30","doi-asserted-by":"publisher","first-page":"3387","DOI":"10.1109\/TCSVT.2020.2967754","volume":"30","author":"S Ge","year":"2020","unstructured":"Ge S, Li C, Zhao S, Zeng D (2020) Occluded face recognition in the wild by identity-diversity inpainting. IEEE Trans Circ Syst Video Technol 30(10):3387\u20133397","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"1619_CR31","doi-asserted-by":"crossref","unstructured":"Li C, Ge S, Zhang D, Li J (2020) Look through masks: towards masked face recognition with de-occlusion distillation. In: Proceedings of the 28th ACM international conference on multimedia, pp 3016\u20133024","DOI":"10.1145\/3394171.3413960"},{"key":"1619_CR32","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.neucom.2020.04.121","volume":"404","author":"X Luo","year":"2020","unstructured":"Luo X, He X, Qing L, Chen X, Liu L, Xu Y (2020) Eyesgan: synthesize human face from human eyes. Neurocomputing 404:213\u2013226","journal-title":"Neurocomputing"},{"key":"1619_CR33","doi-asserted-by":"crossref","unstructured":"Shakeel MS (2022) Bam: a bidirectional attention module for masked face recognition. In: 2022 IEEE international conference on visual communications and image processing (VCIP), pp 1\u20135 . IEEE","DOI":"10.1109\/VCIP56404.2022.10008847"},{"key":"1619_CR34","doi-asserted-by":"crossref","unstructured":"Zhang L, Xiong W, Zhao K, Chen K, Zhong M (2023) Maskdul: data uncertainty learning in masked face recognition. In: ICASSP 2023-2023 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1\u20135 . IEEE","DOI":"10.1109\/ICASSP49357.2023.10097008"},{"key":"1619_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121995","volume":"238","author":"M Zhong","year":"2024","unstructured":"Zhong M, Xiong W, Li D, Chen K, Zhang L (2024) Maskduf: data uncertainty learning in masked face recognition with mask uncertainty fluctuation. Expert Syst Appl 238:121995","journal-title":"Expert Syst Appl"},{"key":"1619_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108473","volume":"124","author":"F Boutros","year":"2022","unstructured":"Boutros F, Damer N, Kirchbuchner F, Kuijper A (2022) Self-restrained triplet loss for accurate masked face recognition. Pattern Recogn 124:108473","journal-title":"Pattern Recogn"},{"key":"1619_CR37","doi-asserted-by":"crossref","unstructured":"Misra D, Nalamada T, Arasanipalai AU, Hou Q (2021) Rotate to attend: convolutional triplet attention module. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 3139\u20133148","DOI":"10.1109\/WACV48630.2021.00318"},{"key":"1619_CR38","unstructured":"Fang Y, Cai Y, Chen J, Zhao J, Tian G, Li G (2023) Cross-layer retrospective retrieving via layer attention. arXiv preprint arXiv:2302.03985"},{"key":"1619_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119371","volume":"215","author":"H Tao","year":"2023","unstructured":"Tao H, Duan Q (2023) An adaptive frame selection network with enhanced dilated convolution for video smoke recognition. Expert Syst Appl 215:119371","journal-title":"Expert Syst Appl"},{"key":"1619_CR40","doi-asserted-by":"crossref","unstructured":"Chang WY, Tsai MY, Lo SC (2021) Ressanet: a hybrid backbone of residual block and self-attention module for masked face recognition. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 1468\u20131476","DOI":"10.1109\/ICCVW54120.2021.00170"},{"key":"1619_CR41","doi-asserted-by":"publisher","first-page":"4534","DOI":"10.1109\/TIFS.2021.3109463","volume":"16","author":"Q Wang","year":"2021","unstructured":"Wang Q, Guo G (2021) Dsa-face: diverse and sparse attentions for face recognition robust to pose variation and occlusion. IEEE Trans Inf Forensics Secur 16:4534\u20134543","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"1619_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2022.103628","volume":"88","author":"MS Shakeel","year":"2022","unstructured":"Shakeel MS, Zhang Y, Wang X, Kang W, Mahmood A (2022) Multi-scale attention guided network for end-to-end face alignment and recognition. J Vis Commun Image Represent 88:103628","journal-title":"J Vis Commun Image Represent"},{"issue":"16","key":"1619_CR43","doi-asserted-by":"publisher","first-page":"7310","DOI":"10.3390\/app11167310","volume":"11","author":"H Deng","year":"2021","unstructured":"Deng H, Feng Z, Qian G, Lv X, Li H, Li G (2021) Mfcosface: a masked-face recognition algorithm based on large margin cosine loss. Appl Sci 11(16):7310","journal-title":"Appl Sci"},{"issue":"3","key":"1619_CR44","first-page":"1","volume":"19","author":"B Huang","year":"2023","unstructured":"Huang B, Wang Z, Wang G, Han Z, Jiang K (2023) Local eyebrow feature attention network for masked face recognition. ACM Trans Multimed Comput Commun Appl 19(3):1\u201319","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"key":"1619_CR45","doi-asserted-by":"crossref","unstructured":"Shakeel MS (2024) Caam: a calibrated augmented attention module for masked face recognition. J Vis Commun Image Represent, 104315","DOI":"10.1016\/j.jvcir.2024.104315"},{"issue":"4","key":"1619_CR46","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2017","unstructured":"Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1619_CR47","doi-asserted-by":"crossref","unstructured":"Zamir SW, Arora A, Khan S, Hayat M, Khan FS, Yang MH (2022) Restormer: efficient transformer for high-resolution image restoration. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 5728\u20135739","DOI":"10.1109\/CVPR52688.2022.00564"},{"key":"1619_CR48","unstructured":"Yi D, Lei Z, Liao S, Li SZ (2014) Learning face representation from scratch. arXiv preprint arXiv:1411.7923"},{"key":"1619_CR49","unstructured":"Huang GB, Mattar M, Berg T, Learned-Miller E (2008) Labeled faces in the wild: a database forstudying face recognition in unconstrained environments. In: Workshop on faces in\u2019real-life\u2019images: detection, alignment, and recognition"},{"key":"1619_CR50","doi-asserted-by":"crossref","unstructured":"Sengupta S, Chen JC, Castillo C, Patel VM, Chellappa R, Jacobs DW (2016) Frontal to profile face verification in the wild. In: 2016 IEEE winter conference on applications of computer vision (WACV), pp 1\u20139 . IEEE","DOI":"10.1109\/WACV.2016.7477558"},{"key":"1619_CR51","doi-asserted-by":"crossref","unstructured":"Moschoglou S, Papaioannou A, Sagonas C, Deng J, Kotsia I, Zafeiriou S (2017) Agedb: the first manually collected, in-the-wild age database. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 51\u201359","DOI":"10.1109\/CVPRW.2017.250"},{"issue":"7","key":"1619_CR52","first-page":"5","volume":"5","author":"T Zheng","year":"2018","unstructured":"Zheng T, Deng W (2018) Cross-pose lfw:a database for studying cross-pose face recognition in unconstrained environments. Beijing Univ Posts Telecommun Tech Rep 5(7):5","journal-title":"Beijing Univ Posts Telecommun Tech Rep"},{"key":"1619_CR53","unstructured":"Zheng T, Deng W, Hu J (2017) Cross-age lfw: a database for studying cross-age face recognition in unconstrained environments. arXiv preprint arXiv:1708.08197"},{"issue":"10","key":"1619_CR54","doi-asserted-by":"publisher","first-page":"6939","DOI":"10.1109\/TPAMI.2021.3098962","volume":"44","author":"H Qiu","year":"2021","unstructured":"Qiu H, Gong D, Li Z, Liu W, Tao D (2021) End2end occluded face recognition by masking corrupted features. IEEE Trans Pattern Anal Mach Intell 44(10):6939\u20136952","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"1619_CR55","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1109\/TBIOM.2023.3242085","volume":"5","author":"Z Wang","year":"2023","unstructured":"Wang Z, Huang B, Wang G, Yi P, Jiang K (2023) Masked face recognition dataset and application. IEEE Trans Biomet Behav Identity Sci 5(2):298\u2013304","journal-title":"IEEE Trans Biomet Behav Identity Sci"},{"key":"1619_CR56","doi-asserted-by":"crossref","unstructured":"Guo J, Zhu X, Lei Z, Li SZ (2018) Face synthesis for eyeglass-robust face recognition. In: Biometric recognition: 13th Chinese conference, CCBR 2018, Urumqi, China, August 11\u201312, 2018, proceedings 13, pp 275\u2013284 . Springer","DOI":"10.1007\/978-3-319-97909-0_30"},{"key":"1619_CR57","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7794\u20137803","DOI":"10.1109\/CVPR.2018.00813"},{"key":"1619_CR58","doi-asserted-by":"crossref","unstructured":"Boutros F, Grebe JH, Kuijper A, Damer N (2023) Idiff-face: synthetic-based face recognition through fizzy identity-conditioned diffusion model. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 19650\u201319661","DOI":"10.1109\/ICCV51070.2023.01800"},{"key":"1619_CR59","doi-asserted-by":"crossref","unstructured":"Phan H, Le CX, Le V, He Y, Nguyen A (2024) Fast and interpretable face identification for out-of-distribution data using vision transformers. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp 6301\u20136311","DOI":"10.1109\/WACV57701.2024.00618"},{"issue":"10","key":"1619_CR60","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang K, Zhang Z, Li Z, Qiao Y (2016) Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process Lett 23(10):1499\u20131503","journal-title":"IEEE Signal Process Lett"},{"key":"1619_CR61","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp 618\u2013626","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-026-01619-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10044-026-01619-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-026-01619-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T10:38:27Z","timestamp":1773484707000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10044-026-01619-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,29]]},"references-count":61,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["1619"],"URL":"https:\/\/doi.org\/10.1007\/s10044-026-01619-w","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"value":"1433-7541","type":"print"},{"value":"1433-755X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,29]]},"assertion":[{"value":"19 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no Conflict of interest to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}},{"value":"The source code of the proposed method is available at","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}],"article-number":"31"}}