{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T05:21:43Z","timestamp":1780636903345,"version":"3.54.1"},"publisher-location":"Cham","reference-count":14,"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_27","type":"book-chapter","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T05:02:56Z","timestamp":1631163776000},"page":"240-247","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Lightweight CNN Using HSIC Fine-Tuning for Fingerprint Liveness Detection"],"prefix":"10.1007","author":[{"given":"Chengsheng","family":"Yuan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,8]]},"reference":[{"key":"27_CR1","doi-asserted-by":"crossref","unstructured":"Marasco, M., Ross, A.: A survey on antispoofing schemes for fingerprint recognition systems. ACM Comput. Surv. (CSUR), 47(2), 1\u201336 (2014)","DOI":"10.1145\/2617756"},{"key":"27_CR2","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Pan, S., Zhan, S., Li, Z., Gao, M., Gao. C.: Fldnet. light dense CNN for fingerprint liveness detection. IEEE Access 8, 84141\u201384152 (2020)","DOI":"10.1109\/ACCESS.2020.2990909"},{"key":"27_CR3","doi-asserted-by":"crossref","unstructured":"Reddy, P.V., Kumar, A., Rahman, S,M,K., Mundra, T.S.: A new antispoofing approach for biometric devices. IEEE Trans. Biomed. Circ. Syst. 2(4), 328\u2013337 (2008)","DOI":"10.1109\/TBCAS.2008.2003432"},{"key":"27_CR4","doi-asserted-by":"crossref","unstructured":"Nogueira, R.F.: de Alencar Lotufo, R., Campos, R.: Machado. fingerprint liveness detection using convolutional neural networks. IEEE Trans. Inf. Forens. Secur. 11(6), 1206\u20131213 (2016)","DOI":"10.1109\/TIFS.2016.2520880"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun. J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Patt. Anal. Mach. Intell. 37(9), 1904\u20131916 (2015)","DOI":"10.1109\/TPAMI.2015.2389824"},{"key":"27_CR6","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/11564089_7","volume-title":"Algorithmic Learning Theory","author":"A Gretton","year":"2005","unstructured":"Gretton, A., Bousquet, O., Smola, A., Sch\u00f6lkopf, B.: Measuring statistical dependence with hilbert-schmidt norms. In: Jain, S., Simon, H.U., Tomita, E. (eds.) ALT 2005. LNCS (LNAI), vol. 3734, pp. 63\u201377. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11564089_7"},{"key":"27_CR7","doi-asserted-by":"crossref","unstructured":"Yambay, D., Ghiani, L., Denti, P., Marcialis, G.L., Roli, F., Schuckers, S.: . Livdet 2011\u2013fingerprint liveness detection competition 2011. In: 2012 5th IAPR International Conference on Biometrics (ICB), pp. 208\u2013215. IEEE (2012)","DOI":"10.1109\/ICB.2012.6199810"},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"Ghiani, L., Yambay, D., Mura, M., Tocco, S., Marcialis, G.L., Roli, F., Schuckcrs, S.: Livdet 2013 fingerprint liveness detection competition 2013. In: 2013 International Conference on Biometrics (ICB), pp. 1\u20136. IEEE (2013)","DOI":"10.1109\/ICB.2013.6613027"},{"key":"27_CR9","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1007\/978-3-319-11740-9_34","volume-title":"Rough Sets and Knowledge Technology","author":"D Yu","year":"2014","unstructured":"Yu, D., Wang, H., Chen, P., Wei, Z.: Mixed pooling for convolutional neural networks. In: Miao, D., Pedrycz, W., \u015al\u0229zak, D., Peters, G., Hu, Q., Wang, R. (eds.) RSKT 2014. LNCS (LNAI), vol. 8818, pp. 364\u2013375. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-11740-9_34"},{"key":"27_CR10","doi-asserted-by":"crossref","unstructured":"Ghiani, L., Hadid, A., Marcialis, G.L., Roli, F.: Fingerprint liveness detection using binarized statistical image features. In: 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1\u20136. IEEE (2013)","DOI":"10.1109\/BTAS.2013.6712708"},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Yuan, C., Sun, X., Lv, R.: Fingerprint liveness detection based on multi-scale LPQ and PCA. China Commun. 13(7), 60\u201365 (2016)","DOI":"10.1109\/CC.2016.7559076"},{"key":"27_CR12","doi-asserted-by":"crossref","unstructured":"Gragnaniello, D., Poggi, G., Sansone, C., Verdoliva, L.: Local contrast phase descriptor for fingerprint liveness detection. Patt. Recogn. 48(4), 1050\u20131058 (2015)","DOI":"10.1016\/j.patcog.2014.05.021"},{"key":"27_CR13","doi-asserted-by":"crossref","unstructured":"Gottschlich, C., Marasco, E., Yang, A.Y., Cukic, B.: Fingerprint liveness detection based on histograms of invariant gradients. In: IEEE International Joint Conference On Biometrics, pp. 1\u20137. IEEE (2014)","DOI":"10.1109\/BTAS.2014.6996224"},{"key":"27_CR14","doi-asserted-by":"crossref","unstructured":"RDubey, R.K., Goh, J., Vrizlynn, L.L.: Thing. fingerprint liveness detection from single image using low-level features and shape analysis. IEEE Trans. Inf. Foren. Secur. 11(7), 1461\u20131475 (2016)","DOI":"10.1109\/TIFS.2016.2535899"}],"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_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T05:10:13Z","timestamp":1631164213000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86608-2_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030866075","9783030866082"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86608-2_27","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)"}}]}}