{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T19:01:21Z","timestamp":1774465281285,"version":"3.50.1"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T00:00:00Z","timestamp":1662940800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T00:00:00Z","timestamp":1662940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2018YFC0807302"],"award-info":[{"award-number":["2018YFC0807302"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772032"],"award-info":[{"award-number":["61772032"]}],"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":["31971789"],"award-info":[{"award-number":["31971789"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009558","name":"University Natural Science Research Project of Anhui Province","doi-asserted-by":"publisher","award":["KJ2019A0027"],"award-info":[{"award-number":["KJ2019A0027"]}],"id":[{"id":"10.13039\/501100009558","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1007\/s00521-022-07777-2","type":"journal-article","created":{"date-parts":[[2022,9,12]],"date-time":"2022-09-12T17:03:10Z","timestamp":1663002190000},"page":"375-392","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Multi-scale confusion and filling mechanism for pressure footprint recognition"],"prefix":"10.1007","volume":"35","author":[{"given":"Yan","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Yongsheng","family":"Sun","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9923-0062","authenticated-orcid":false,"given":"Nian","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zijian","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Tang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,12]]},"reference":[{"issue":"5","key":"7777_CR1","first-page":"959","volume":"50","author":"W Bao","year":"2020","unstructured":"Bao W, Qu J, Wang N et al (2020) Force-tactile footprint recognition based on spatial aggregation weighted convolutional neural network. J Southeast Univ (Natl Sci Edn) 50(5):959\u2013964 (in Chinese)","journal-title":"J Southeast Univ (Natl Sci Edn)"},{"key":"7777_CR2","doi-asserted-by":"publisher","first-page":"26,641","DOI":"10.1109\/ACCESS.2021.3057931","volume":"9","author":"S Basheer","year":"2021","unstructured":"Basheer S, Nagwanshi K, Bhatia S et al (2021) Fesd: an approach for biometric human footprint matching using fuzzy ensemble learning. IEEE Access 9:26,641-26,663. https:\/\/doi.org\/10.1109\/ACCESS.2021.3057931","journal-title":"IEEE Access"},{"key":"7777_CR3","doi-asserted-by":"publisher","first-page":"4683","DOI":"10.1109\/TIP.2020.2973812","volume":"29","author":"D Chang","year":"2020","unstructured":"Chang D, Ding Y, Xie J et al (2020) The devil is in the channels: mutual-channel loss for fine-grained image classification. IEEE Trans Image Process 29:4683\u20134695. https:\/\/doi.org\/10.1109\/TIP.2020.2973812","journal-title":"IEEE Trans Image Process"},{"key":"7777_CR4","doi-asserted-by":"publisher","unstructured":"Chattopadhay A, Sarkar A, Howlader P, et\u00a0al (2018) Grad-cam++: generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE winter conference on applications of computer vision (WACV), IEEE, pp 839\u2013847. https:\/\/doi.org\/10.1109\/WACV.2018.00097","DOI":"10.1109\/WACV.2018.00097"},{"key":"7777_CR5","doi-asserted-by":"publisher","first-page":"029","DOI":"10.1155\/2021\/6631029","volume":"6631","author":"D Chen","year":"2021","unstructured":"Chen D, Chen Y, Ma J et al (2021) An ensemble deep neural network for footprint image retrieval based on transfer learning. J Sens 6631:029. https:\/\/doi.org\/10.1155\/2021\/6631029","journal-title":"J Sens"},{"key":"7777_CR6","doi-asserted-by":"publisher","unstructured":"Chen Y, Bai Y, Zhang W, et\u00a0al (2019) Destruction and construction learning for fine-grained image recognition. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 5152\u20135161. https:\/\/doi.org\/10.1109\/CVPR.2019.00530","DOI":"10.1109\/CVPR.2019.00530"},{"key":"7777_CR7","unstructured":"DeVries T, Taylor G (2017) Improved regularization of convolutional neural networks with cutout. Preprint arXiv:1708.04552"},{"key":"7777_CR8","doi-asserted-by":"publisher","unstructured":"Ding Y, Zhou Y, Zhu Y, et\u00a0al (2019) Selective sparse sampling for fine-grained image recognition. In: 2019 IEEE\/CVF international conference on computer vision (ICCV), pp 6598\u20136607. https:\/\/doi.org\/10.1109\/ICCV.2019.00670","DOI":"10.1109\/ICCV.2019.00670"},{"key":"7777_CR9","doi-asserted-by":"publisher","unstructured":"Dubey A, Gupta O, Guo P, et\u00a0al (2018) Pairwise confusion for fine-grained visual classification. In: Computer Vision\u2014ECCV 2018\u201415th European Conference, Munich, Germany, September 8\u201314, 2018, Proceedings, Part XII, pp 71\u201388. https:\/\/doi.org\/10.1007\/978-3-030-01258-8_5","DOI":"10.1007\/978-3-030-01258-8_5"},{"key":"7777_CR10","unstructured":"Dubey A, Gupta O, Raskar R, et\u00a0al (2018) Maximum-entropy fine grained classification. In: Advances in neural information processing systems 31, pp 637\u2013647. http:\/\/papers.nips.cc\/paper\/7344-maximum-entropy-fine-grained-classification.pdf"},{"key":"7777_CR11","doi-asserted-by":"publisher","unstructured":"Fu J, Zheng H, Mei T (2017) Look closer to see better: recurrent attention convolutional neural network for fine-grained image recognition. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 4476\u20134484. https:\/\/doi.org\/10.1109\/CVPR.2017.476","DOI":"10.1109\/CVPR.2017.476"},{"key":"7777_CR12","doi-asserted-by":"publisher","unstructured":"Gao Y, Beijbom O, Zhang N, et\u00a0al (2016) Compact bilinear pooling. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 317\u2013326. https:\/\/doi.org\/10.1109\/CVPR.2016.41","DOI":"10.1109\/CVPR.2016.41"},{"issue":"107","key":"7777_CR13","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1016\/j.asoc.2021.107680","volume":"111","author":"S Gergely","year":"2021","unstructured":"Gergely S, L\u00f3r\u00e1nt F (2021) Data augmentation by guided deep interpolation. Appl Soft Comput 111(107):680. https:\/\/doi.org\/10.1016\/j.asoc.2021.107680","journal-title":"Appl Soft Comput"},{"key":"7777_CR14","doi-asserted-by":"publisher","first-page":"2672","DOI":"10.1145\/3422622","volume":"3","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M et al (2014) Generative adversarial networks. Adv Neural Inf Process Syst 3:2672\u20132680. https:\/\/doi.org\/10.1145\/3422622","journal-title":"Adv Neural Inf Process Syst"},{"issue":"4","key":"7777_CR15","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1016\/j.gaitpost.2007.07.002","volume":"27","author":"J Gurney","year":"2008","unstructured":"Gurney J, Kersting U, Rosenbaum D (2008) Between-day reliability of repeated plantar pressure distribution measurements in a normal population. Gait Posture 27(4):706\u2013709. https:\/\/doi.org\/10.1016\/j.gaitpost.2007.07.002","journal-title":"Gait Posture"},{"key":"7777_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07417-9","author":"F Han","year":"2022","unstructured":"Han F, Zhu S, Ling Q et al (2022) Gene-cwgan: a data enhancement method for gene expression profile based on improved cwgan-gp. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-022-07417-9","journal-title":"Neural Comput Appl"},{"key":"7777_CR17","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, et\u00a0al (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"7777_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06163-8","author":"G Huang","year":"2021","unstructured":"Huang G, Jafari A (2021) Enhanced balancing gan: minority-class image generation. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-021-06163-8","journal-title":"Neural Comput Appl"},{"key":"7777_CR19","doi-asserted-by":"publisher","unstructured":"Huang G, Liu Z, Van Der Maaten L, et\u00a0al (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708. https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"7777_CR20","doi-asserted-by":"publisher","unstructured":"Huang S, Xu Z, Tao D, et\u00a0al (2016) Part-stacked CNN for fine-grained visual categorization. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 1173\u20131182. https:\/\/doi.org\/10.1109\/CVPR.2016.132","DOI":"10.1109\/CVPR.2016.132"},{"key":"7777_CR21","doi-asserted-by":"publisher","unstructured":"Jia D, Wei D, Richard S, et\u00a0al (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, pp 248\u2013255. https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"7777_CR22","doi-asserted-by":"publisher","first-page":"1855","DOI":"10.1007\/s00521-011-0530-9","volume":"21","author":"W Jia","year":"2012","unstructured":"Jia W, Cai H, Gui J et al (2012) Newborn footprint recognition using orientation feature. Neural Comput Appl 21:1855\u20131863. https:\/\/doi.org\/10.1007\/s00521-011-0530-9","journal-title":"Neural Comput Appl"},{"key":"7777_CR23","doi-asserted-by":"publisher","unstructured":"Khokher R, Singh R, Kumar R (2015) Footprint recognition with principal component analysis and independent component analysis. In: Macromolecular symposia, pp 16\u201326. https:\/\/doi.org\/10.1002\/masy.201400045","DOI":"10.1002\/masy.201400045"},{"key":"7777_CR24","unstructured":"Khosla A, Jayadevaprakash N, Yao B, et\u00a0al (2011) Novel dataset for fine-grained image categorization. In: First workshop on fine-grained visual categorization, IEEE conference on computer vision and pattern recognition. http:\/\/people.csail.mit.edu\/khosla\/papers\/fgvc2011.pdf"},{"key":"7777_CR25","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1145\/3065386","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097\u20131105. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Adv Neural Inf Process Syst"},{"key":"7777_CR26","doi-asserted-by":"publisher","unstructured":"Kulkarni P, Kulkarni V (2015) Human footprint classification using image parameters. In: 2015 international conference on pervasive computing (ICPC), pp 1\u20135. https:\/\/doi.org\/10.1109\/PERVASIVE.2015.7087011","DOI":"10.1109\/PERVASIVE.2015.7087011"},{"key":"7777_CR27","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.1007\/s10462-020-09887-6","volume":"54","author":"R Kushwaha","year":"2021","unstructured":"Kushwaha R, Singal G, Nain N (2021) A texture feature based approach for person verification using footprint bio-metric. Artif Intell Rev 54:1581\u20131611. https:\/\/doi.org\/10.1007\/s10462-020-09887-6","journal-title":"Artif Intell Rev"},{"key":"7777_CR28","doi-asserted-by":"publisher","unstructured":"Lin D, Shen X, Lu C, et\u00a0al (2015) Deep LAC: deep localization, alignment and classification for fine-grained recognition. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 1666\u20131674. https:\/\/doi.org\/10.1109\/CVPR.2015.7298775","DOI":"10.1109\/CVPR.2015.7298775"},{"key":"7777_CR29","doi-asserted-by":"publisher","unstructured":"Lin T, RoyChowdhury A, Maji S (2015) Bilinear CNN models for fine-grained visual recognition. In: 2015 IEEE international conference on computer vision (ICCV), pp 1449\u20131457. https:\/\/doi.org\/10.1109\/ICCV.2015.170","DOI":"10.1109\/ICCV.2015.170"},{"key":"7777_CR30","unstructured":"Liu X, Xia T, Wang J, et\u00a0al (2016) Fully convolutional attention localization networks: efficient attention localization for fine-grained recognition. CoRR arXiv:abs\/1603.06765"},{"key":"7777_CR31","unstructured":"Loshchilov I, Hutter F (2017) Sgdr: stochastic gradient descent with warm restarts. In: 5th international conference on learning representations, ICLR 2017, Toulon, France, April 24\u201326, 2017, Conference Track Proceedings. https:\/\/openreview.net\/forum?id=Skq89Scxx"},{"key":"7777_CR32","unstructured":"Maji S, Kannala J, Rahtu E, et\u00a0al (2013) Fine-grained visual classification of aircraft. CoRR arXiv:abs\/1306.5151"},{"key":"7777_CR33","unstructured":"Mariani G, Scheidegger F, Istrate R, et\u00a0al (2018) Bagan: data augmentation with balancing gan. arXiv:1803.09655"},{"issue":"6","key":"7777_CR34","doi-asserted-by":"publisher","first-page":"1476","DOI":"10.1109\/TSMCB.2008.927722","volume":"38","author":"S Moustakidis","year":"2008","unstructured":"Moustakidis S, Theocharis J, Giakas G (2008) Subject recognition based on ground reaction force measurements of gait signals. IEEE Trans Syst Man Cybern Part B (Cybern) 38(6):1476\u20131485. https:\/\/doi.org\/10.1109\/TSMCB.2008.927722","journal-title":"IEEE Trans Syst Man Cybern Part B (Cybern)"},{"key":"7777_CR35","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.forsciint.2018.07.022","volume":"290","author":"D Nguyen","year":"2018","unstructured":"Nguyen D, Phan C, Koo S (2018) Predicting body movements for person identification under different walking conditions. For Sci Int 290:303\u2013309. https:\/\/doi.org\/10.1016\/j.forsciint.2018.07.022","journal-title":"For Sci Int"},{"key":"7777_CR36","unstructured":"Odena A, Olah C, Shlens J (2017) Conditional image synthesis with auxiliary classifier gans. In: Proceedings of the 34th international conference on machine learning, vol\u00a070. PMLR, pp 2642\u20132651. http:\/\/proceedings.mlr.press\/v70\/odena17a.html"},{"key":"7777_CR37","doi-asserted-by":"publisher","unstructured":"Osisanwo F, Adetunmbi A, \u00c1lese B (2014) Barefoot morphology: a person unique feature for forensic identification. In: The 9th international conference for internet technology and secured transactions (ICITST-2014), pp 356\u2013359. https:\/\/doi.org\/10.1109\/ICITST.2014.7038837","DOI":"10.1109\/ICITST.2014.7038837"},{"key":"7777_CR38","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. In: 3rd international conference on learning representations, ICLR 2015, San Diego, CA, USA, May 7\u20139, 2015, Conference Track Proceedings. arXiv:1409.1556"},{"key":"7777_CR39","doi-asserted-by":"publisher","unstructured":"Summers C, Dinneen M (2019) Improved mixed-example data augmentation. In: 2019 IEEE winter conference on applications of computer vision (WACV), pp 1262\u20131270. https:\/\/doi.org\/10.1109\/WACV.2019.00139","DOI":"10.1109\/WACV.2019.00139"},{"key":"7777_CR40","doi-asserted-by":"publisher","unstructured":"Sun M, Yuan Y, Zhou F, et\u00a0al (2018) Multi-attention multi-class constraint for fine-grained image recognition. In: Computer Vision\u2014ECCV 2018\u201415th European Conference, Munich, Germany, September 8\u201314, 2018, Proceedings, Part XVI, pp 834\u2013850. https:\/\/doi.org\/10.1007\/978-3-030-01270-0_49","DOI":"10.1007\/978-3-030-01270-0_49"},{"key":"7777_CR41","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.neucom.2017.12.020","volume":"282","author":"Q Sun","year":"2018","unstructured":"Sun Q, Wang Q, Zhang J et al (2018) Hyperlayer bilinear pooling with application to fine-grained categorization and image retrieval. Neurocomputing 282:174\u2013183. https:\/\/doi.org\/10.1016\/j.neucom.2017.12.020","journal-title":"Neurocomputing"},{"key":"7777_CR42","doi-asserted-by":"publisher","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, et\u00a0al (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826. https:\/\/doi.org\/10.1109\/CVPR.2016.308","DOI":"10.1109\/CVPR.2016.308"},{"key":"7777_CR43","unstructured":"Wah C, Branson S, Welinder P, et\u00a0al (2011) The Caltech\u2013UCSD Birds-200-2011 Dataset. Tech. Rep. CNS-TR-2011-001, California Institute of Technology. http:\/\/www.vision.caltech.edu\/visipedia\/papers\/CUB_200_2011.pdf"},{"issue":"6","key":"7777_CR44","first-page":"106","volume":"48","author":"N Wang","year":"2020","unstructured":"Wang N, Chen X, Zhang Y et al (2020) FtH-Net method for predicting height based on footprint image. J South China Univ Technol (Natl Sci Edn) 48(6):106\u2013113 (in Chinese)","journal-title":"J South China Univ Technol (Natl Sci Edn)"},{"key":"7777_CR45","doi-asserted-by":"publisher","first-page":"14,613","DOI":"10.1007\/s00521-020-05148-3","volume":"32","author":"W Wang","year":"2020","unstructured":"Wang W, Cui Y, Li G et al (2020) A self-attention-based destruction and construction learning fine-grained image classification method for retail product recognition. Neural Comput Appl 32:14,613-14,622. https:\/\/doi.org\/10.1007\/s00521-020-05148-3","journal-title":"Neural Comput Appl"},{"issue":"4","key":"7777_CR46","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik A, Sheikh H et al (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612. https:\/\/doi.org\/10.1109\/TIP.2003.819861","journal-title":"IEEE Trans Image Process"},{"key":"7777_CR47","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1016\/j.patcog.2017.10.002","volume":"76","author":"X Wei","year":"2018","unstructured":"Wei X, Xie C, Wu J et al (2018) Mask-CNN: localizing parts and selecting descriptors for fine-grained bird species categorization. Pattern Recogn 76:704\u2013714. https:\/\/doi.org\/10.1016\/j.patcog.2017.10.002","journal-title":"Pattern Recogn"},{"key":"7777_CR48","doi-asserted-by":"publisher","unstructured":"Wei X, Zhang Y, Gong Y, et\u00a0al (2018) Grassmann pooling as compact homogeneous bilinear pooling for fine-grained visual classification. In: Computer Vision\u2014ECCV 2018\u201415th European Conference, Munich, Germany, September 8\u201314, 2018, Proceedings, Part III, pp 365\u2013380. https:\/\/doi.org\/10.1007\/978-3-030-01219-9_22","DOI":"10.1007\/978-3-030-01219-9_22"},{"key":"7777_CR49","unstructured":"Wei X, Wu J, Cui Q (2019) Deep learning for fine-grained image analysis: a survey. CoRR arXiv:1907.03069"},{"issue":"6","key":"7777_CR50","first-page":"529","volume":"26","author":"R Xia","year":"2013","unstructured":"Xia R, Ma Z, Yao Z et al (2013) Gait recognition based on spatio-temporal hog feature of plantar pressure distribution. J Pattern Recogn Artif Intell 26(6):529\u2013536 (in Chinese)","journal-title":"J Pattern Recogn Artif Intell"},{"key":"7777_CR51","doi-asserted-by":"publisher","unstructured":"Yang Z, Luo T, Wang D, et\u00a0al (2018) Learning to navigate for fine-grained classification. In: Computer Vision\u2014ECCV 2018\u201415th European Conference, Munich, Germany, September 8\u201314, 2018, Proceedings, Part XIV, pp 438\u2013454. https:\/\/doi.org\/10.1007\/978-3-030-01264-9_26","DOI":"10.1007\/978-3-030-01264-9_26"},{"key":"7777_CR52","doi-asserted-by":"publisher","unstructured":"Yu C, Zhao X, Zheng Q, et\u00a0al (2018) Hierarchical bilinear pooling for fine-grained visual recognition. In: Computer Vision\u2014ECCV 2018\u201415th European Conference, Munich, Germany, September 8\u201314, 2018, Proceedings, Part XVI, pp 595\u2013610. https:\/\/doi.org\/10.1007\/978-3-030-01270-0_35","DOI":"10.1007\/978-3-030-01270-0_35"},{"key":"7777_CR53","doi-asserted-by":"publisher","unstructured":"Yun S, Han D, Oh S, et\u00a0al (2019) Cutmix: Regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 6023\u20136032. https:\/\/doi.org\/10.1109\/ICCV.2019.00612","DOI":"10.1109\/ICCV.2019.00612"},{"issue":"5","key":"7777_CR54","doi-asserted-by":"publisher","first-page":"419","DOI":"10.18280\/ts.360506","volume":"36","author":"C Zhang","year":"2019","unstructured":"Zhang C, Pan S, Qi Y et al (2019) A footprint extraction and recognition algorithm based on plantar pressure. Traitement du Signal (5) 36(5):419\u2013424. https:\/\/doi.org\/10.18280\/ts.360506","journal-title":"Traitement du Signal (5)"},{"key":"7777_CR55","unstructured":"Zhang H, Ciss\u00e9 M, Dauphin Y, et\u00a0al (2018) mixup: beyond empirical risk minimization. In: 6th international conference on learning representations, ICLR 2018, Vancouver, BC, Canada, April 30\u2013May 3, 2018, Conference Track Proceedings. https:\/\/openreview.net\/forum?id=r1Ddp1-Rb"},{"key":"7777_CR56","doi-asserted-by":"publisher","unstructured":"Zhang N, Donahue J, Girshick R, et\u00a0al (2014) Part-based R-CNNs for fine-grained category detection. In: Computer Vision\u2014ECCV 2014\u201413th European Conference, Zurich, Switzerland, September 6\u201312, 2014, Proceedings, Part I, pp 834\u2013849. https:\/\/doi.org\/10.1007\/978-3-319-10590-1_54","DOI":"10.1007\/978-3-319-10590-1_54"},{"key":"7777_CR57","doi-asserted-by":"publisher","unstructured":"Zhang X, Xiong H, Zhou W, et\u00a0al (2016) Picking deep filter responses for fine-grained image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 1134\u20131142. https:\/\/doi.org\/10.1109\/CVPR.2016.128","DOI":"10.1109\/CVPR.2016.128"},{"issue":"5","key":"7777_CR58","first-page":"73","volume":"47","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Wang Q, Wang N et al (2019) Footprint recognition algorithm based on multi-modal features. J Huazhong Univ of Sci Tech (Natl Sci Edn) 47(5):73\u201378 (in Chinese)","journal-title":"J Huazhong Univ of Sci Tech (Natl Sci Edn)"},{"issue":"6","key":"7777_CR59","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1109\/TMM.2017.2648498","volume":"19","author":"B Zhao","year":"2017","unstructured":"Zhao B, Wu X, Feng J et al (2017) Diversified visual attention networks for fine-grained object classification. IEEE Trans Multimed 19(6):1245\u20131256. https:\/\/doi.org\/10.1109\/TMM.2017.2648498","journal-title":"IEEE Trans Multimed"},{"key":"7777_CR60","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1060\/1\/012047","author":"F Zhao","year":"2018","unstructured":"Zhao F (2018) Biometric identification technology and development trend of physiological characteristics. J Phys Conf Ser. https:\/\/doi.org\/10.1088\/1742-6596\/1060\/1\/012047","journal-title":"J Phys Conf Ser"},{"key":"7777_CR61","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.neucom.2018.02.109","volume":"395","author":"J Zhao","year":"2020","unstructured":"Zhao J, Peng Y, He X (2020) Attribute hierarchy based multi-task learning for fine-grained image classification. Neurocomputing 395:150\u2013159. https:\/\/doi.org\/10.1016\/j.neucom.2018.02.109","journal-title":"Neurocomputing"},{"key":"7777_CR62","doi-asserted-by":"publisher","unstructured":"Zheng H, Fu J, Mei T, et\u00a0al (2017) Learning multi-attention convolutional neural network for fine-grained image recognition. In: 2017 IEEE international conference on computer vision (ICCV), pp 5219\u20135227. https:\/\/doi.org\/10.1109\/ICCV.2017.557","DOI":"10.1109\/ICCV.2017.557"},{"key":"7777_CR63","doi-asserted-by":"publisher","unstructured":"Zheng H, Fu J, Zha Z, et\u00a0al (2019) Looking for the Devil in the Details: Learning trilinear attention sampling network for fine-grained image recognition. In: 2019 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), pp 5007\u20135016. https:\/\/doi.org\/10.1109\/CVPR.2019.00515","DOI":"10.1109\/CVPR.2019.00515"},{"key":"7777_CR64","doi-asserted-by":"crossref","unstructured":"Zhong Z, Zheng L, Kang G, et\u00a0al (2020) Random erasing data augmentation. In: Proceedings of the AAAI conference on artificial intelligence, pp 13,001\u201313,008. https:\/\/aaai.org\/ojs\/index.php\/AAAI\/article\/view\/7000","DOI":"10.1609\/aaai.v34i07.7000"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07777-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07777-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07777-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T06:09:35Z","timestamp":1673071775000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07777-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,12]]},"references-count":64,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["7777"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07777-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,12]]},"assertion":[{"value":"16 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"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":"Conflict of interest"}}]}}