{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T15:33:11Z","timestamp":1771515191693,"version":"3.50.1"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030866075","type":"print"},{"value":"9783030866082","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-86608-2_22","type":"book-chapter","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T05:02:56Z","timestamp":1631163776000},"page":"195-202","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Finger Vein Recognition Using a Shallow Convolutional Neural Network"],"prefix":"10.1007","author":[{"given":"Jiazhen","family":"Liu","sequence":"first","affiliation":[]},{"given":"Ziyan","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Kaiyang","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Minjie","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhen","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Xinwei","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Yicheng","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Yuncong","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Zhe","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Hakil","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Changlong","family":"Jin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,8]]},"reference":[{"issue":"2","key":"22_CR1","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."},{"key":"22_CR2","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1109\/TIFS.2019.2928507","volume":"15","author":"W Kang","year":"2020","unstructured":"Kang, W., Liu, H., Luo, W., Deng, F.: Study of a full-view 3D finger vein verification technique. IEEE Trans. Inf. Forensics Secur. 15, 1175\u20131189 (2020)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"8","key":"22_CR3","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."},{"key":"22_CR4","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.neucom.2018.02.042","volume":"290","author":"Y Fang","year":"2018","unstructured":"Fang, Y., Wu, Q., Kang, W.: A novel finger vein verification system based on two-stream convolutional network learning. Neurocomputing 290, 100\u2013107 (2018)","journal-title":"Neurocomputing"},{"key":"22_CR5","doi-asserted-by":"publisher","first-page":"35113","DOI":"10.1109\/ACCESS.2019.2902429","volume":"7","author":"Y Lu","year":"2019","unstructured":"Lu, Y., Xie, S., Wu, S.: Exploring competitive features using deep convolutional neural network for finger vein recognition. IEEE Access 7, 35113\u201335123 (2019)","journal-title":"IEEE Access"},{"key":"22_CR6","doi-asserted-by":"publisher","first-page":"66845","DOI":"10.1109\/ACCESS.2019.2918503","volume":"7","author":"JM Song","year":"2019","unstructured":"Song, J.M., Kim, W., Park, K.R.: Finger-vein recognition based on deep DenseNet using composite image. IEEE Access 7, 66845\u201366863 (2019)","journal-title":"IEEE Access"},{"issue":"5","key":"22_CR7","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1049\/iet-bmt.2018.5245","volume":"8","author":"S Tang","year":"2019","unstructured":"Tang, S., Zhou, S., Kang, W., Wu, Q., Deng, F.: Finger vein verification using a Siamese CNN. IET Biometrics 8(5), 306\u2013315 (2019)","journal-title":"IET Biometrics"},{"key":"22_CR8","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.patrec.2017.12.001","volume":"119","author":"C Xie","year":"2019","unstructured":"Xie, C., Kumar, A.: Finger vein identification using convolutional neural network and supervised discrete hashing. Pattern Recogn. Lett. 119, 148\u2013156 (2019)","journal-title":"Pattern Recogn. Lett."},{"issue":"9","key":"22_CR9","doi-asserted-by":"publisher","first-page":"2512","DOI":"10.1109\/TIFS.2019.2902819","volume":"14","author":"W Yang","year":"2019","unstructured":"Yang, W., Hui, C., Chen, Z., Xue, J., Liao, Q.: V-GAN: finger vein representation using generative adversarial networks. IEEE Trans. Inf. Forensics Secur. 14(9), 2512\u20132524 (2019)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"22_CR10","first-page":"1863","volume":"24","author":"S Radzi","year":"2016","unstructured":"Radzi, S., Hani, M., Bakhteri, R.: Finger-vein biometric identification using convolutional neural network. Turk. J. Electr. 24, 1863\u20131878 (2016)","journal-title":"Turk. J. Electr."},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Fairuz, S., Habaebi, M.H., Elsheikh, E.M.A., Chebil, A.J.: convolutional neural network-based finger vein recognition using near infrared images. In: 2018 7th International Conference on Computer and Communication Engineering (ICCCE), pp. 453\u2013458 (2018)","DOI":"10.1109\/ICCCE.2018.8539342"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Hou, B., Yan, R.: Convolutional auto-encoder based deep feature learning for finger-vein verification. In: IEEE International Symposium on Medical Measurements and Applications (MeMeA) 2018, pp. 1\u20135 (2018)","DOI":"10.1109\/MeMeA.2018.8438719"},{"issue":"6","key":"22_CR13","doi-asserted-by":"publisher","first-page":"1297","DOI":"10.3390\/s17061297","volume":"17","author":"HG Hong","year":"2017","unstructured":"Hong, H.G., Lee, M.B., Park, K.R.: Convolutional neural network-based finger-vein recognition using NIR image sensors. Sensors 17(6), 1297 (2017)","journal-title":"Sensors"},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Hu, H., et al.: FV-Net: learning a finger-vein feature representation based on a CNN. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 3489\u20133494 (2018)","DOI":"10.1109\/ICPR.2018.8546007"},{"issue":"7","key":"22_CR15","doi-asserted-by":"publisher","first-page":"2296","DOI":"10.3390\/s18072296","volume":"18","author":"W Kim","year":"2018","unstructured":"Kim, W., Song, M.J., Park, R.K.: Multimodal biometric recognition based on convolutional neural network by the fusion of finger-vein and finger shape using near-infrared (NIR) camera sensor. Sensors 18(7), 2296 (2018)","journal-title":"Sensors"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Jalilian, E., Uhl, A.: Enhanced segmentation-CNN based finger-vein recognition by joint training with automatically generated and manual labels. In: 2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA), pp. 1\u20138 (2019)","DOI":"10.1109\/ISBA.2019.8778522"},{"key":"22_CR17","unstructured":"Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch. Computer Science (2014)"},{"key":"22_CR18","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.patrec.2018.12.006","volume":"117","author":"K Su","year":"2019","unstructured":"Su, K., Yang, G., Yang, L., Li, D., Su, P., Yin, Y.: Learning binary hash codes for finger vein image retrieval. Pattern Recogn. Lett. 117, 74\u201382 (2019)","journal-title":"Pattern Recogn. Lett."},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Jalilian, E., Uhl, A.: Finger-vein recognition using deep fully convolutional neural semantic segmentation networks: the impact of training data. Presented at the 2018 IEEE International Workshop on Information Forensics and Security (WIFS) (2018)","DOI":"10.1109\/WIFS.2018.8630794"},{"issue":"7","key":"22_CR20","doi-asserted-by":"publisher","first-page":"926","DOI":"10.1109\/LSP.2018.2822810","volume":"25","author":"F Wang","year":"2018","unstructured":"Wang, F., Cheng, J., Liu, W., Liu, H.: Additive margin softmax for face verification. IEEE Signal Process. Lett. 25(7), 926\u2013930 (2018)","journal-title":"IEEE Signal Process. Lett."},{"key":"22_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1007\/978-3-642-25449-9_33","volume-title":"Biometric Recognition","author":"Y Yin","year":"2011","unstructured":"Yin, Y., Liu, L., Sun, X.: SDUMLA-HMT: a multimodal biometric database. In: Sun, Z., Lai, J., Chen, X., Tan, T. (eds.) CCBR 2011. LNCS, vol. 7098, pp. 260\u2013268. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-25449-9_33"},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Lu, Y., Xie, S.J., Yoon, S., Wang, Z., Park, D.S.: An available database for the research of finger vein recognition. In: 2013 6th International Congress on Image and Signal Processing (CISP), vol. 01, pp. 410\u2013415 (2013)","DOI":"10.1109\/CISP.2013.6744030"}],"container-title":["Lecture Notes in Computer Science","Biometric Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86608-2_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T05:09:04Z","timestamp":1631164144000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86608-2_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030866075","9783030866082"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86608-2_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"8 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCBR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Biometric Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","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":"10 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccbr2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ccbr99.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72","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":"53","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":"74% - 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":"2.3","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":"2.1","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)"}},{"value":"Full papers are up to 11 pages long.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}