{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T22:53:18Z","timestamp":1743029598752,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":34,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811692468"},{"type":"electronic","value":"9789811692475"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-16-9247-5_20","type":"book-chapter","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T21:25:33Z","timestamp":1641936333000},"page":"261-275","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Ensemble Deep Learning Based Single Finger-Vein Recognition"],"prefix":"10.1007","author":[{"given":"Chongwen","family":"Liu","sequence":"first","affiliation":[]},{"given":"Huafeng","family":"Qin","sequence":"additional","affiliation":[]},{"given":"Gongping","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Zhengwen","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"20_CR1","unstructured":"Albrecht, T., L\u00fcthi, M., Vetter, T., Chen, H., Houmani, N.: Encyclopedia of Biometrics (2009)"},{"issue":"7","key":"20_CR2","doi-asserted-by":"publisher","first-page":"3367","DOI":"10.1016\/j.eswa.2013.11.033","volume":"41","author":"MSM Asaari","year":"2014","unstructured":"Asaari, M.S.M., Suandi, S.A., Rosdi, B.A.: Fusion of band limited phase only correlation and width centroid contour distance for finger based biometrics. Expert Syst. Appl. 41(7), 3367\u20133382 (2014)","journal-title":"Expert Syst. Appl."},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Avci, A., Kocakulak, M., Acir, N.: Convolutional neural network designs for finger-vein-based biometric identification. In: 2019 11th International Conference on Electrical and Electronics Engineering (ELECO), pp. 580\u2013584 (2019)","DOI":"10.23919\/ELECO47770.2019.8990612"},{"key":"20_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.optlaseng.2017.03.007","volume":"95","author":"A Chatterjee","year":"2017","unstructured":"Chatterjee, A., Bhatia, V., Prakash, S.: Anti-spoof touchless 3D fingerprint recognition system using single shot fringe projection and biospeckle analysis. Opt. Lasers Eng. 95, 1\u20137 (2017)","journal-title":"Opt. Lasers Eng."},{"key":"20_CR5","doi-asserted-by":"publisher","first-page":"219","DOI":"10.4028\/www.scientific.net\/AMM.145.219","volume":"145","author":"SR Cho","year":"2012","unstructured":"Cho, S.R., et al.: Enhancement of finger-vein image by vein line tracking and adaptive gabor filtering for finger-vein recognition. Appl. Mech. Mater. 145, 219\u2013223 (2012)","journal-title":"Appl. Mech. Mater."},{"issue":"2","key":"20_CR6","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1109\/TIFS.2018.2850320","volume":"14","author":"R Das","year":"2019","unstructured":"Das, R., Piciucco, E., Maiorana, E., Campisi, P.: Convolutional neural network for finger-vein-based biometric identification. IEEE Trans. Inf. Forensics Secur. 14(2), 360\u2013373 (2019)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"4","key":"20_CR7","doi-asserted-by":"publisher","first-page":"1002","DOI":"10.1109\/TPAMI.2017.2700390","volume":"40","author":"C Ding","year":"2018","unstructured":"Ding, C., Tao, D.: Trunk-branch ensemble convolutional neural networks for video-based face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 1002\u20131014 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Gumusbas, D., Yildirim, T., Kocakulak, M., Acir, N.: Capsule network for finger-vein-based biometric identification. In: 2019 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 437\u2013441 (2019)","DOI":"10.1109\/SSCI44817.2019.9003019"},{"key":"20_CR9","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.engappai.2017.02.002","volume":"60","author":"S Joardar","year":"2017","unstructured":"Joardar, S., Chatterjee, A., Bandyopadhyay, S., Maulik, U.: Multi-size patch based collaborative representation for palm dorsa vein pattern recognition by enhanced ensemble learning with modified interactive artificial bee colony algorithm. Eng. Appl. Artif. Intell. 60, 151\u2013163 (2017)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"20_CR10","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.infrared.2014.10.007","volume":"68","author":"W Kang","year":"2015","unstructured":"Kang, W., Chen, X., Qiuxia, W.: The biometric recognition on contactless multi-spectrum finger images. Infrared Phys. Technol. 68, 19\u201327 (2015)","journal-title":"Infrared Phys. Technol."},{"issue":"2","key":"20_CR11","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1016\/j.patcog.2014.08.024","volume":"48","author":"SH Khan","year":"2015","unstructured":"Khan, S.H., Akbar, M.A., Shahzad, F., Farooq, M., Khan, Z.: Secure biometric template generation for multi-factor authentication. Pattern Recognit. 48(2), 458\u2013472 (2015)","journal-title":"Pattern Recognit."},{"issue":"4","key":"20_CR12","doi-asserted-by":"publisher","first-page":"2228","DOI":"10.1109\/TIP.2011.2171697","volume":"21","author":"A Kumar","year":"2012","unstructured":"Kumar, A., Zhou, Y.: Human identification using finger images. IEEE Trans. Image Process. 21(4), 2228\u20132244 (2012)","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"20_CR13","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.3390\/s110302319","volume":"11","author":"EC Lee","year":"2011","unstructured":"Lee, E.C., Jung, H., Kim, D.: New finger biometric method using near infrared imaging. Sensors 11(3), 2319\u20132333 (2011)","journal-title":"Sensors"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Li, Q., et al.: A multi-task learning based approach to biomedical entity relation extraction. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 680\u2013682 (2018)","DOI":"10.1109\/BIBM.2018.8621284"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Lou, Y., Fu, G., Jiang, Z., Men, A., Zhou, Y.: Improve object detection via a multi-feature and multi-task CNN model. In: 2017 IEEE Visual Communications and Image Processing (VCIP), pp. 1\u20134 (2017)","DOI":"10.1109\/VCIP.2017.8305022"},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Misra, I., Shrivastava, A., Gupta, A., Hebert, M.: Cross-stitch networks for multi-task learning. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.433"},{"issue":"4","key":"20_CR17","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1007\/s00138-004-0149-2","volume":"15","author":"N Miura","year":"2004","unstructured":"Miura, N., Nagasaka, A., Miyatake, T.: Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification. Mach. Vis. Appl. 15(4), 194\u2013203 (2004)","journal-title":"Mach. Vis. Appl."},{"issue":"8","key":"20_CR18","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1093\/ietisy\/e90-d.8.1185","volume":"90","author":"N Miura","year":"2007","unstructured":"Miura, N., Nagasaka, A., Miyatake, T.: Extraction of finger-vein patterns using maximum curvature points in image profiles. ICE Trans. Inf. Syst. 90(8), 1185\u20131194 (2007)","journal-title":"ICE Trans. Inf. Syst."},{"issue":"8","key":"20_CR19","doi-asserted-by":"publisher","first-page":"1677","DOI":"10.1109\/TCSVT.2017.2684826","volume":"28","author":"H Qin","year":"2018","unstructured":"Qin, H., El-Yacoubi, M.A.: Deep representation for finger-vein image-quality assessment. IEEE Trans. Circuits Syst. Video Technol. 28(8), 1677\u20131693 (2018)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"20_CR20","doi-asserted-by":"publisher","first-page":"34823","DOI":"10.1109\/ACCESS.2019.2901335","volume":"7","author":"H Qin","year":"2019","unstructured":"Qin, H., El Yacoubi, M.A., Lin, J., Liu, B.: An iterative deep neural network for hand-vein verification. IEEE Access 7, 34823\u201334837 (2019)","journal-title":"IEEE Access"},{"issue":"8","key":"20_CR21","doi-asserted-by":"publisher","first-page":"1816","DOI":"10.1109\/TIFS.2017.2689724","volume":"12","author":"H Qin","year":"2017","unstructured":"Qin, H., El-Yacoubi, M.A.: Deep representation-based feature extraction and recovering for finger-vein verification. IEEE Trans. Inf. Forensics Secur. 12(8), 1816\u20131829 (2017)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"11","key":"20_CR22","doi-asserted-by":"publisher","first-page":"15048","DOI":"10.3390\/s131115048","volume":"13","author":"H Qin","year":"2013","unstructured":"Qin, H., Qin, L., Xue, L., He, X., Chengbo, Yu., Liang, X.: Finger-vein verification based on multi-features fusion. Sensors 13(11), 15048\u201315067 (2013)","journal-title":"Sensors"},{"issue":"5","key":"20_CR23","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1117\/1.3572129","volume":"50","author":"H Qin","year":"2011","unstructured":"Qin, H., Qin, L., Chengbo, Yu.: Region growth-based feature extraction method for finger-vein recognition. Opt. Eng. 50(5), 214\u2013229 (2011)","journal-title":"Opt. Eng."},{"issue":"5","key":"20_CR24","doi-asserted-by":"crossref","first-page":"e1249","DOI":"10.1002\/widm.1249","volume":"8","author":"O Sagi","year":"2018","unstructured":"Sagi, O., Rokach, L.: Ensemble learning: a survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 8(5), e1249 (2018)","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"20_CR25","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1016\/j.neucom.2020.12.072","volume":"432","author":"H Shao","year":"2021","unstructured":"Shao, H., Zhong, D.: One-shot cross-dataset palmprint recognition via adversarial domain adaptation. Neurocomputing 432, 288\u2013299 (2021)","journal-title":"Neurocomputing"},{"issue":"11","key":"20_CR26","doi-asserted-by":"publisher","first-page":"1541","DOI":"10.1016\/j.patrec.2011.04.021","volume":"32","author":"W Song","year":"2011","unstructured":"Song, W., et al.: A finger-vein verification system using mean curvature. Pattern Recognit. Lett. 32(11), 1541\u20131547 (2011)","journal-title":"Pattern Recognit. Lett."},{"key":"20_CR27","unstructured":"Vodinh, T.: Biomedical Photonics Handbook, 2nd edn (2012)"},{"key":"20_CR28","doi-asserted-by":"crossref","unstructured":"Wang, L., Li, Y., Wang, S.: Feature learning for one-shot face recognition. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 2386\u20132390 (2018)","DOI":"10.1109\/ICIP.2018.8451464"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Wu, Z., Deng, W.: One-shot deep neural network for pose and illumination normalization face recognition. In: 2016 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136 (2016)","DOI":"10.1109\/ICME.2016.7552902"},{"key":"20_CR30","doi-asserted-by":"publisher","first-page":"183118","DOI":"10.1109\/ACCESS.2019.2960411","volume":"7","author":"J Zhang","year":"2019","unstructured":"Zhang, J., Lu, Z., Li, M., Wu, H.: Gan-based image augmentation for finger-vein biometric recognition. IEEE Access 7, 183118\u2013183132 (2019)","journal-title":"IEEE Access"},{"issue":"8","key":"20_CR31","doi-asserted-by":"publisher","first-page":"2378","DOI":"10.1109\/TIP.2011.2109730","volume":"20","author":"L Zhang","year":"2011","unstructured":"Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378\u20132386 (2011)","journal-title":"IEEE Trans. Image Process."},{"issue":"9","key":"20_CR32","doi-asserted-by":"publisher","first-page":"1990","DOI":"10.1016\/j.patcog.2010.06.007","volume":"44","author":"L Zhang","year":"2011","unstructured":"Zhang, L., Zhang, L., Zhang, D., Zhu, H.: Ensemble of local and global information for finger-knuckle-print recognition. Pattern Recognit. 44(9), 1990\u20131998 (2011). Computer Analysis of Images and Patterns","journal-title":"Pattern Recognit."},{"key":"20_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, W., Zhang, L., Lu, Y.: Adaptive gabor convolutional neural networks for finger-vein recognition. In: 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD IS), pp. 219\u2013222 (2019)","DOI":"10.1109\/HPBDIS.2019.8735471"},{"key":"20_CR34","doi-asserted-by":"publisher","first-page":"159821","DOI":"10.1109\/ACCESS.2019.2950698","volume":"7","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Li, W., Zhang, L., Ning, X., Sun, L., Lu, Y.: Adaptive learning gabor filter for finger-vein recognition. IEEE Access 7, 159821\u2013159830 (2019)","journal-title":"IEEE Access"}],"container-title":["Communications in Computer and Information Science","Cognitive Systems and Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-9247-5_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T18:17:46Z","timestamp":1700072266000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-9247-5_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811692468","9789811692475"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-9247-5_20","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"11 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Cognitive Systems and Signal Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Suzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iccsip2021.tsingzhan.com\/#\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"105","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}