{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T14:43:52Z","timestamp":1779893032693,"version":"3.53.1"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"28","license":[{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971086"],"award-info":[{"award-number":["61971086"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61471082"],"award-info":[{"award-number":["61471082"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s00521-023-08800-w","type":"journal-article","created":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T04:02:06Z","timestamp":1690862526000},"page":"21071-21091","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Structure correlation-aware attention for Iris recognition"],"prefix":"10.1007","volume":"35","author":[{"given":"Lingyao","family":"Jia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiule","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peihua","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,8,1]]},"reference":[{"issue":"9","key":"8800_CR1","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1109\/5.628669","volume":"85","author":"RP Wildes","year":"1997","unstructured":"Wildes RP (1997) Iris recognition: an emerging biometric technology. Proc. IEEE 85(9):1348\u20131363","journal-title":"Proc. IEEE"},{"issue":"1","key":"8800_CR2","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TCSVT.2003.818349","volume":"14","author":"AK Jain","year":"2004","unstructured":"Jain AK, Ross A, Prabhakar S (2004) An introduction to biometric recognition. IEEE Trans. Circuits Syst. Video Technol. 14(1):4\u201320","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"7","key":"8800_CR3","doi-asserted-by":"crossref","first-page":"2267","DOI":"10.1007\/s00521-018-3754-0","volume":"32","author":"N Ahmadi","year":"2020","unstructured":"Ahmadi N, Akbarizadeh G (2020) Iris tissue recognition based on gldm feature extraction and hybrid mlpnn-ica classifier. Neural Comput. Appl. 32(7):2267\u20132281","journal-title":"Neural Comput. Appl."},{"issue":"12","key":"8800_CR4","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1109\/TPAMI.2003.1251145","volume":"25","author":"L Ma","year":"2003","unstructured":"Ma L, Tan T, Wang Y, Zhang D (2003) Personal identification based on iris texture analysis. IEEE Trans. Pattern Anal. Mach. Intell. 25(12):1519\u20131533","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"12","key":"8800_CR5","first-page":"2211","volume":"31","author":"Z Sun","year":"2008","unstructured":"Sun Z, Tan T (2008) Ordinal measures for iris recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(12):2211\u20132226","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"8800_CR6","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1109\/TIFS.2015.2500196","volume":"11","author":"J Daugman","year":"2015","unstructured":"Daugman J (2015) Information theory and the iriscode. IEEE Trans. Inf. Forensic Secur. 11(2):400\u2013409","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"issue":"8","key":"8800_CR7","doi-asserted-by":"crossref","first-page":"6039","DOI":"10.1007\/s00521-021-06690-4","volume":"34","author":"O Elharrouss","year":"2022","unstructured":"Elharrouss O, Almaadeed N, Al-Maadeed S, Khelifi F (2022) Pose-invariant face recognition with multitask cascade networks. Neural Comput. Appl. 34(8):6039\u20136052","journal-title":"Neural Comput. Appl."},{"issue":"11","key":"8800_CR8","doi-asserted-by":"crossref","first-page":"8617","DOI":"10.1007\/s00521-021-06758-1","volume":"34","author":"BU Maheswari","year":"2022","unstructured":"Maheswari BU, Rajakumar M, Ramya J (2022) Dynamic differential annealing-based anti-spoofing model for fingerprint detection using cnn. Neural Comput. Appl. 34(11):8617\u20138633","journal-title":"Neural Comput. Appl."},{"key":"8800_CR9","volume":"132","author":"L Shen","year":"2022","unstructured":"Shen L, Zhang Y, Zhao K, Zhang R, Shen W (2022) Distribution alignment for cross-device palmprint recognition. Pattern Recognit. 132:108942","journal-title":"Pattern Recognit."},{"issue":"11","key":"8800_CR10","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1109\/34.244676","volume":"15","author":"JG Daugman","year":"1993","unstructured":"Daugman JG (1993) High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15(11):1148\u20131161","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"8800_CR11","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1023\/A:1012365806338","volume":"45","author":"J Daugman","year":"2001","unstructured":"Daugman J (2001) Statistical richness of visual phase information: update on recognizing persons by iris patterns. Int. J. Comput. Vis. 45(1):25\u201338","journal-title":"Int. J. Comput. Vis."},{"issue":"1477","key":"8800_CR12","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1098\/rspb.2001.1696","volume":"268","author":"J Daugman","year":"2001","unstructured":"Daugman J, Downing C (2001) Epigenetic randomness, complexity and singularity of human iris patterns. Proc. R. Soc. B-Biol. Sci. 268(1477):1737\u20131740","journal-title":"Proc. R. Soc. B-Biol. Sci."},{"issue":"1","key":"8800_CR13","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TCSVT.2003.818350","volume":"14","author":"J Daugman","year":"2004","unstructured":"Daugman J (2004) How iris recognition works. IEEE Trans. Circuits Syst. Video Technol. 14(1):21\u201330","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"11","key":"8800_CR14","doi-asserted-by":"crossref","first-page":"5609","DOI":"10.1007\/s00521-020-05342-3","volume":"33","author":"M Choudhary","year":"2021","unstructured":"Choudhary M, Tiwari V, Uduthalapally V (2021) Iris presentation attack detection based on best-k feature selection from yolo inspired roi. Neural Comput. Appl. 33(11):5609\u20135629","journal-title":"Neural Comput. Appl."},{"key":"8800_CR15","unstructured":"Nguyen K, Proen\u00e7a H, Alonso-Fernandez F (2022) Deep learning for iris recognition: A survey. arXiv:2210.05866"},{"key":"8800_CR16","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1016\/j.inffus.2022.10.032","volume":"91","author":"Z Qin","year":"2023","unstructured":"Qin Z, Zhao P, Zhuang T, Deng F, Ding Y, Chen D (2023) A survey of identity recognition via data fusion and feature learning. Inf. Fusion 91:694\u2013712","journal-title":"Inf. Fusion"},{"key":"8800_CR17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-022-10237-x","volume":"56","author":"S Minaee","year":"2023","unstructured":"Minaee S, Abdolrashidi A, Su H, Bennamoun M, Zhang D (2023) Biometrics recognition using deep learning: a survey. Artif. Intell. Rev. 56:1\u201349","journal-title":"Artif. Intell. Rev."},{"key":"8800_CR18","doi-asserted-by":"crossref","unstructured":"He Z, Sun Z, Tan T, Qiu X, Zhong C, Dong W (2008) Boosting ordinal features for accurate and fast iris recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20138 (2008)","DOI":"10.1109\/CVPR.2008.4587645"},{"key":"8800_CR19","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.neucom.2017.12.053","volume":"330","author":"M Zhang","year":"2019","unstructured":"Zhang M, He Z, Zhang H, Tan T, Sun Z (2019) Toward practical remote iris recognition: a boosting based framework. Neurocomputing 330:238\u2013252","journal-title":"Neurocomputing"},{"key":"8800_CR20","doi-asserted-by":"crossref","unstructured":"Hu Y, Sirlantzis K, Howells G (2016) A study on iris textural correlation using steering kernels. In: IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1\u20138","DOI":"10.1109\/BTAS.2016.7791160"},{"issue":"1","key":"8800_CR21","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/TIFS.2016.2606083","volume":"12","author":"Y Hu","year":"2016","unstructured":"Hu Y, Sirlantzis K, Howells G (2016) Optimal generation of iris codes for iris recognition. IEEE Trans. Inf. Forensic Secur. 12(1):157\u2013171","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"issue":"3","key":"8800_CR22","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1049\/iet-bmt.2018.5199","volume":"8","author":"J Daugman","year":"2019","unstructured":"Daugman J, Downing C (2019) Radial correlations in iris patterns, and mutual information within iriscodes. IET Biom. 8(3):185\u2013189","journal-title":"IET Biom."},{"issue":"10","key":"8800_CR23","doi-asserted-by":"crossref","first-page":"2373","DOI":"10.1109\/TIFS.2017.2686013","volume":"12","author":"N Liu","year":"2017","unstructured":"Liu N, Liu J, Sun Z, Tan T (2017) A code-level approach to heterogeneous iris recognition. IEEE Trans. Inf. Forensic Secur. 12(10):2373\u20132386","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"key":"8800_CR24","doi-asserted-by":"crossref","unstructured":"Minaee S, Abdolrashidi A, Wang Y (2015). Iris recognition using scattering transform and textural features. In 2015 IEEE signal processing and signal processing education workshop (SP\/SPE) (pp. 37-42). IEEE","DOI":"10.1109\/DSP-SPE.2015.7369524"},{"key":"8800_CR25","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.future.2020.01.056","volume":"107","author":"S Adamovi\u0107","year":"2020","unstructured":"Adamovi\u0107 S, Mi\u0161kovic V, Ma\u010dek N, Milosavljevi\u0107 M, \u0160arac M, Sara\u010devi\u0107 M, Gnjatovi\u0107 M (2020) An efficient novel approach for iris recognition based on stylometric features and machine learning techniques. Futur. Gener. Comp. Syst. 107:144\u2013157","journal-title":"Futur. Gener. Comp. Syst."},{"key":"8800_CR26","doi-asserted-by":"crossref","first-page":"2944","DOI":"10.1109\/TIFS.2020.2980791","volume":"15","author":"C Wang","year":"2020","unstructured":"Wang C, Muhammad J, Wang Y, He Z, Sun Z (2020) Towards complete and accurate iris segmentation using deep multi-task attention network for non-cooperative iris recognition. IEEE Trans. Inf. Forensic Secur. 15:2944\u20132959","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"key":"8800_CR27","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1109\/TIFS.2020.3023289","volume":"16","author":"K Wang","year":"2020","unstructured":"Wang K, Kumar A (2020) Periocular-assisted multi-feature collaboration for dynamic iris recognition. IEEE Trans. Inf. Forensic Secur. 16:866\u2013879","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"key":"8800_CR28","doi-asserted-by":"crossref","first-page":"7166","DOI":"10.1109\/TIP.2020.2999211","volume":"29","author":"K Nguyen","year":"2020","unstructured":"Nguyen K, Fookes C, Sridharan S (2020) Constrained design of deep iris networks. IEEE Trans. Image Process. 29:7166\u20137175","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"8800_CR29","doi-asserted-by":"crossref","first-page":"3810","DOI":"10.1109\/TCSVT.2021.3117291","volume":"32","author":"J Wei","year":"2022","unstructured":"Wei J, Wang Y, Li Y, He R, Sun Z (2022) Cross-spectral iris recognition by learning device-specific band. IEEE Trans. Circuits Syst. Video Technol. 32(6):3810\u20133824","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"1","key":"8800_CR30","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/TPAMI.2022.3152857","volume":"45","author":"K Nguyen","year":"2022","unstructured":"Nguyen K, Fookes C, Sridharan S, Ross A (2022) Complex-valued iris recognition network. IEEE Trans. Pattern Anal. Mach. Intell. 45(1):182\u2013196","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8800_CR31","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1109\/TIFS.2022.3154240","volume":"17","author":"J Wei","year":"2022","unstructured":"Wei J, Huang H, Wang Y, He R, Sun Z (2022) Towards more discriminative and robust iris recognition by learning uncertain factors. IEEE Trans. Inf. Forensic Secur. 17:865\u2013879","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"key":"8800_CR32","doi-asserted-by":"crossref","unstructured":"Minaee S, Abdolrashidiy A, Wang Y (2016) An experimental study of deep convolutional features for iris recognition. In 2016 IEEE signal processing in medicine and biology symposium (SPMB) (pp. 1-6). IEEE","DOI":"10.1109\/SPMB.2016.7846859"},{"key":"8800_CR33","doi-asserted-by":"crossref","unstructured":"Proencca H, Neves JC (2017) IRINA: Iris recognition (even) in inaccurately segmented data. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 538-547)","DOI":"10.1109\/CVPR.2017.714"},{"issue":"12","key":"8800_CR34","doi-asserted-by":"crossref","first-page":"3233","DOI":"10.1109\/TIFS.2019.2913234","volume":"14","author":"K Wang","year":"2019","unstructured":"Wang K, Kumar A (2019) Toward more accurate iris recognition using dilated residual features. IEEE Trans. Inf. Forensic Secur. 14(12):3233\u20133245","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"issue":"4","key":"8800_CR35","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1109\/TBIOM.2021.3102736","volume":"3","author":"M Mostofa","year":"2021","unstructured":"Mostofa M, Mohamadi S, Dawson J, Nasrabadi NM (2021) Deep gan-based cross-spectral cross-resolution iris recognition. IEEE Trans. Biom. Behav. Iden. Sci. 3(4):443\u2013463","journal-title":"IEEE Trans. Biom. Behav. Iden. Sci."},{"key":"8800_CR36","doi-asserted-by":"crossref","unstructured":"Ren M, Wang Y, Sun Z, Tan T (2020) Dynamic graph representation for occlusion handling in biometrics. In Proceedings of the AAAI Conference on Artificial Intelligence 34(07):11940-11947","DOI":"10.1609\/aaai.v34i07.6869"},{"key":"8800_CR37","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1109\/TIFS.2022.3221897","volume":"18","author":"J Wei","year":"2022","unstructured":"Wei J, Wang Y, Huang H, He R, Sun Z, Gao X (2022) Contextual measures for iris recognition. IEEE Trans. Inf. Forensic Secur. 18:57\u201370","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"key":"8800_CR38","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.neucom.2022.10.064","volume":"517","author":"Y Chen","year":"2023","unstructured":"Chen Y, Gan H, Chen H, Zeng Y, Xu L, Heidari AA, Zhu X, Liu Y (2023) Accurate iris segmentation and recognition using an end-to-end unified framework based on madnet and dsanet. Neurocomputing 517:264\u2013278","journal-title":"Neurocomputing"},{"key":"8800_CR39","doi-asserted-by":"crossref","unstructured":"Khan SK, Tinsley P, Czajka A (2023) DeformIrisNet: An Identity-Preserving Model of Iris Texture Deformation. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (pp. 900-908)","DOI":"10.1109\/WACV56688.2023.00096"},{"issue":"10","key":"8800_CR40","doi-asserted-by":"crossref","first-page":"11273","DOI":"10.1007\/s10489-021-02925-y","volume":"52","author":"L Jia","year":"2022","unstructured":"Jia L, Shi X, Sun Q, Tang X, Li P (2022) Second-order convolutional networks for iris recognition. Appl Intell 52(10):11273\u201311287","journal-title":"Appl Intell"},{"issue":"8","key":"8800_CR41","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","volume":"42","author":"J Hu","year":"2020","unstructured":"Hu J, Shen L, Albanie S, Sun G, Wu E (2020) Squeeze-and-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. 42(8):2011\u20132023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8800_CR42","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. 11534-11542)","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"8800_CR43","doi-asserted-by":"crossref","unstructured":"Yang Z, Zhu L, Wu Y, Yang Y (2020) Gated channel transformation for visual recognition. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 11794-11803)","DOI":"10.1109\/CVPR42600.2020.01181"},{"key":"8800_CR44","doi-asserted-by":"crossref","unstructured":"Yang G, Zeng H, Li P, Zhang L (2015) High-order information for robust iris recognition under less controlled conditions. In 2015 IEEE International Conference on Image Processing (ICIP) (pp. 4535-4539). IEEE","DOI":"10.1109\/ICIP.2015.7351665"},{"key":"8800_CR45","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1007\/978-3-319-69923-3_42","volume-title":"Chinese conference on biometric recognition","author":"X Tang","year":"2017","unstructured":"Tang X, Xie J, Li P (2017) Deep convolutional features for iris recognition. Chinese conference on biometric recognition. Springer, Cham, pp 391\u2013400"},{"key":"8800_CR46","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. 3-19)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"8800_CR47","doi-asserted-by":"crossref","unstructured":"Zhang Z, Lan C, Zeng W, Jin X, Chen Z (2020) Relation-aware global attention for person re-identification. In Proceedings of the ieee\/cvf conference on computer vision and pattern recognition (pp. 3186-3195)","DOI":"10.1109\/CVPR42600.2020.00325"},{"key":"8800_CR48","doi-asserted-by":"crossref","unstructured":"Zhu Z, Xu M, Bai S, Huang T, Bai X (2019) Asymmetric non-local neural networks for semantic segmentation. In Proceedings of the IEEE\/CVF international conference on computer vision (pp. 593-602)","DOI":"10.1109\/ICCV.2019.00068"},{"key":"8800_CR49","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.neucom.2021.03.003","volume":"445","author":"Q Sun","year":"2021","unstructured":"Sun Q, Zhang Z, Li P (2021) Second-order encoding networks for semantic segmentation. Neurocomputing 445:50\u201360","journal-title":"Neurocomputing"},{"key":"8800_CR50","doi-asserted-by":"crossref","unstructured":"Gao Z, Xie J, Wang Q, Li P (2019) Global second-order pooling convolutional networks. In Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition (pp. 3024-3033)","DOI":"10.1109\/CVPR.2019.00314"},{"key":"8800_CR51","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 13713-13722)","DOI":"10.1109\/CVPR46437.2021.01350"},{"issue":"4","key":"8800_CR52","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1007\/s11263-019-01283-0","volume":"128","author":"J Park","year":"2020","unstructured":"Park J, Woo S, Lee J-Y, Kweon IS (2020) A simple and light-weight attention module for convolutional neural networks. Int. J. Comput. Vis. 128(4):783\u2013798","journal-title":"Int. J. Comput. Vis."},{"key":"8800_CR53","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. 7794-7803)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"8800_CR54","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. 3146-3154)","DOI":"10.1109\/CVPR.2019.00326"},{"issue":"9","key":"8800_CR55","first-page":"4626","volume":"44","author":"P Fang","year":"2021","unstructured":"Fang P, Zhou J, Roy SK, Ji P, Petersson L, Harandi M (2021) Attention in attention networks for person retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 44(9):4626\u20134641","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8800_CR56","doi-asserted-by":"crossref","unstructured":"Vaswani A, Ramachandran P, Srinivas A, Parmar N, Hechtman B, Shlens J (2021) Scaling local self-attention for parameter efficient visual backbones. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 12894-12904)","DOI":"10.1109\/CVPR46437.2021.01270"},{"key":"8800_CR57","unstructured":"Yang L, Zhang RY, Li L, Xie X (2021) Simam: A simple, parameter-free attention module for convolutional neural networks. In International conference on machine learning (pp. 11863-11874). PMLR"},{"key":"8800_CR58","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv. Neural Inform. Process. Syst. 25:1097\u20131105","journal-title":"Adv. Neural Inform. Process. Syst."},{"issue":"2","key":"8800_CR59","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.imavis.2009.04.010","volume":"28","author":"P Li","year":"2010","unstructured":"Li P, Liu X, Xiao L, Song Q (2010) Robust and accurate iris segmentation in very noisy iris images. Image Vis. Comput. 28(2):246\u2013253","journal-title":"Image Vis. Comput."},{"key":"8800_CR60","doi-asserted-by":"crossref","unstructured":"Li P, Xie J, Wang Q, Gao Z (2018) Towards faster training of global covariance pooling networks by iterative matrix square root normalization. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 947-955)","DOI":"10.1109\/CVPR.2018.00105"},{"key":"8800_CR61","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778)","DOI":"10.1109\/CVPR.2016.90"},{"key":"8800_CR62","doi-asserted-by":"crossref","unstructured":"Wen Y, Zhang K, Li Z, Qiao Y (2016). A discriminative feature learning approach for deep face recognition. In Computer Vision ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, 2016, Proceedings, Part VII 14 (pp. 499-515)","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"8800_CR63","unstructured":"Hermans A, Beyer L, Leibe B (2017) In defense of the triplet loss for person re-identification. arXiv:1703.07737"},{"key":"8800_CR64","doi-asserted-by":"crossref","unstructured":"Schroff F, Kalenichenko D, Philbin J (2015) Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 815-823)","DOI":"10.1109\/CVPR.2015.7298682"},{"issue":"5","key":"8800_CR65","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1109\/TPAMI.2009.59","volume":"32","author":"PJ Phillips","year":"2009","unstructured":"Phillips PJ, Scruggs WT, O\u2019Toole AJ, Flynn PJ, Bowyer KW, Schott CL, Sharpe M (2009) Frvt 2006 and ice 2006 large-scale experimental results. IEEE Trans. Pattern Anal. Mach. Intell. 32(5):831\u2013846","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8800_CR66","unstructured":"Biometrics Ideal Test. CASIA.v4 Database. Accessed: 2002. [Online]. Available: http:\/\/biometrics.idealtest.org"},{"issue":"12","key":"8800_CR67","doi-asserted-by":"crossref","first-page":"2465","DOI":"10.1109\/TPAMI.2011.89","volume":"33","author":"KP Hollingsworth","year":"2011","unstructured":"Hollingsworth KP, Bowyer KW, Flynn PJ (2011) Improved iris recognition through fusion of hamming distance and fragile bit distance. IEEE Trans. Pattern Anal. Mach. Intell. 33(12):2465\u20132476","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8800_CR68","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708)","DOI":"10.1109\/CVPR.2017.243"},{"key":"8800_CR69","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.patrec.2015.09.002","volume":"82","author":"N Othman","year":"2016","unstructured":"Othman N, Dorizzi B, Garcia-Salicetti S (2016) Osiris: an open source iris recognition software. Pattern Recognit. Lett. 82:124\u2013131","journal-title":"Pattern Recognit. Lett."},{"key":"8800_CR70","doi-asserted-by":"crossref","unstructured":"Gangwar A, Joshi A (2016) DeepIrisNet: Deep iris representation with applications in iris recognition and cross-sensor iris recognition. In 2016 IEEE international conference on image processing (ICIP) (pp. 2301-2305). IEEE","DOI":"10.1109\/ICIP.2016.7532769"},{"key":"8800_CR71","doi-asserted-by":"crossref","unstructured":"Yang K, Xu Z, Fei J (2021) Dualsanet: Dual spatial attention network for iris recognition. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (pp. 889-897)","DOI":"10.1109\/WACV48630.2021.00093"},{"issue":"1","key":"8800_CR72","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1109\/TFUZZ.2019.2912576","volume":"28","author":"M Liu","year":"2019","unstructured":"Liu M, Zhou Z, Shang P, Xu D (2019) Fuzzified image enhancement for deep learning in iris recognition. IEEE Trans. Fuzzy Syst. 28(1):92\u201399","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"8800_CR73","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2021.104112","volume":"107","author":"Y Chen","year":"2021","unstructured":"Chen Y, Wu C, Wang Y (2021) Whether normalized or not? towards more robust iris recognition using dynamic programming. Image Vis. Comput. 107:104112","journal-title":"Image Vis. Comput."}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08800-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08800-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08800-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T12:26:13Z","timestamp":1729859173000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08800-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,1]]},"references-count":73,"journal-issue":{"issue":"28","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["8800"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08800-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,1]]},"assertion":[{"value":"9 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2023","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 declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}