{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T19:21:58Z","timestamp":1743016918932,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030869595"},{"type":"electronic","value":"9783030869601"}],"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\/978-3-030-86960-1_17","type":"book-chapter","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T07:02:59Z","timestamp":1631257379000},"page":"237-250","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identifying the Origin of Finger Vein Samples Using Texture Descriptors"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1662-8324","authenticated-orcid":false,"given":"Babak","family":"Maser","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5921-8755","authenticated-orcid":false,"given":"Andreas","family":"Uhl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,11]]},"reference":[{"issue":"1","key":"17_CR1","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1109\/TIFS.2007.916285","volume":"3","author":"M Chen","year":"2008","unstructured":"Chen, M., Fridrich, J., Goljan, M., Luk\u00e1s, J.: Determining image origin and integrity using sensor noise. IEEE Trans. Inf. Forensics Secur. 3(1), 74\u201390 (2008)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"17_CR2","doi-asserted-by":"crossref","unstructured":"Uhl, A., H\u00f6ller, Y.: Iris-sensor authentication using camera PRNU fingerprints. In: Proceedings of the 5th IAPR\/IEEE International Conference on Biometrics, ICB 2012, New Delhi, March 2012, India, pp. 1\u20138 (2012)","DOI":"10.1109\/ICB.2012.6199813"},{"key":"17_CR3","doi-asserted-by":"crossref","unstructured":"Goljan, M., Fridrich, J., Chen, M.: Sensor noise camera identification: countering counter-forensics. In: Media Forensics and Security II, vol. 7541, p. 75410S. International Society for Optics and Photonics (2010)","DOI":"10.1117\/12.839055"},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Kalka, N., Bartlow, N., Cukic, B., Ross, A.: A preliminary study on identifying sensors from iris images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 50\u201356 (2015)","DOI":"10.1109\/CVPRW.2015.7301319"},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Debiasi, L., Sun, Z., Uhl, A.: Generation of iris sensor PRNU fingerprints from uncorrelated data. In: Proceedings of the 2nd International Workshop on Biometrics and Forensics, IWBF 2014, Valletta, Malta, pp. 1\u20136 (2014)","DOI":"10.1109\/IWBF.2014.6914262"},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"S\u00f6llinger, D., Maser, B., Uhl, A.: PRNU-based finger vein sensor identification: on the effect of different sensor croppings. In: 2019 International Conference on Biometrics (ICB), pp. 1\u20138 (2019)","DOI":"10.1109\/ICB45273.2019.8987237"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"S\u00f6llinger, D., Debiasi, L., Uhl, A.: Can you really trust the sensor\u2019s PRNU? How image content might impact the finger vein sensor identification performance. In: Proceedings of the 25th International Conference on Pattern Recognition (ICPR), pp. 7782\u20137789 (2020)","DOI":"10.1109\/ICPR48806.2021.9412194"},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"Debiasi, L., Uhl, A.: Techniques for a forensic analysis of the CASIA-Iris V4 database. In: Proceedings of the 3rd International Workshop on Biometrics and Forensics, IWBF 2015, Gjovik, Norway, pp. 1\u20138 (2015)","DOI":"10.1109\/IWBF.2015.7110236"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Debiasi, L., Kauba, C., Uhl, A.: Identifying iris sensors from iris images. In: Iris and Periocular Biometric Recognition, vol. 5, p. 359 (2017)","DOI":"10.1049\/PBSE005E_ch16"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"El-Naggar, S., Ross, A.: Which dataset is this iris image from? In: 2015 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1\u20136. IEEE (2015)","DOI":"10.1109\/WIFS.2015.7368570"},{"key":"17_CR11","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"},{"issue":"4","key":"17_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."},{"key":"17_CR13","unstructured":"Tome, P., Vanoni, M., Marcel, S.: On the vulnerability of finger vein recognition to spoofing. In: IEEE International Conference of the Biometrics Special Interest Group (BIOSIG) (September 2014)"},{"key":"17_CR14","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), CISP 2013, vol. 1, pp. 410\u2013415. IEEE (2013)","DOI":"10.1109\/CISP.2013.6744030"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Kauba, C., Prommegger, B., Uhl, A.: Focussing the beam - a new laser illumination based data set providing insights to finger-vein recognition. In: Proceedings of the IEEE 9th International Conference on Biometrics: Theory, Applications, and Systems, BTAS 2018, Los Angeles, California, USA, pp. 1\u20139 (2018)","DOI":"10.1109\/BTAS.2018.8698588"},{"issue":"7","key":"17_CR16","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., Rosdi, B.A., Suandi, S.A.: Fusion of band limited phase only correlation and width centroid contour distance for finger based biometrics. Exp. Syst. Appl. 41(7), 3367\u20133382 (2014)","journal-title":"Exp. Syst. Appl."},{"key":"17_CR17","doi-asserted-by":"crossref","unstructured":"Ton, B.T., Veldhuis, R.N.J.: A high quality finger vascular pattern dataset collected using acustom designed capturing device. In: International Conference on Biometrics, ICB 2013. IEEE (2013)","DOI":"10.1109\/ICB.2013.6612966"},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"S\u00f6llinger, D., Maser, B., Uhl, A.: PRNU-based finger vein sensor identification: on the effect of different sensor croppings. In: 2019 International Conference on Biometrics (ICB), pp. 1\u20138. IEEE (2019)","DOI":"10.1109\/ICB45273.2019.8987237"},{"issue":"2","key":"17_CR19","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1109\/83.902291","volume":"10","author":"TF Chan","year":"2001","unstructured":"Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266\u2013277 (2001)","journal-title":"IEEE Trans. Image Process."},{"key":"17_CR20","doi-asserted-by":"publisher","first-page":"214","DOI":"10.5201\/ipol.2012.g-cv","volume":"2","author":"P Getreuer","year":"2012","unstructured":"Getreuer, P.: Chan-Vese segmentation. IPOL J. 2, 214\u2013224 (2012)","journal-title":"IPOL J."},{"key":"17_CR21","doi-asserted-by":"publisher","first-page":"S68","DOI":"10.1016\/j.cmpb.2009.02.017","volume":"95","author":"A V\u00e9csei","year":"2009","unstructured":"V\u00e9csei, A., Fuhrmann, T., Liedlgruber, M., Brunauer, L., Payer, H., Uhl, A.: Automated classification of duodenal imagery in celiac disease using evolved Fourier feature vectors. Comput. Meth. Program. Biomed. 95, S68\u2013S78 (2009)","journal-title":"Comput. Meth. Program. Biomed."},{"issue":"1","key":"17_CR22","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/0031-3203(95)00067-4","volume":"29","author":"T Ojala","year":"1996","unstructured":"Ojala, T., Pietik\u00e4inen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51\u201359 (1996)","journal-title":"Pattern Recogn."},{"key":"17_CR23","first-page":"e453","volume":"2","author":"F Boulogne","year":"2014","unstructured":"Boulogne, F., Warner, J.D., Yager, E.N.: scikit-image: Image processing in Python. PerrJ 2, e453 (2014)","journal-title":"PerrJ"},{"key":"17_CR24","unstructured":"Kohavi, R., et al.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI, vol. 14, Montreal, Canada, pp. 1137\u20131145 (1995)"},{"key":"17_CR25","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1007\/978-3-540-39857-8_12","volume-title":"Machine Learning: ECML 2003","author":"C Ferri","year":"2003","unstructured":"Ferri, C., Hern\u00e1ndez-Orallo, J., Salido, M.A.: Volume under the ROC surface for multi-class problems. In: Lavra\u010d, N., Gamberger, D., Blockeel, H., Todorovski, L. (eds.) ECML 2003. LNCS (LNAI), vol. 2837, pp. 108\u2013120. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/978-3-540-39857-8_12"},{"key":"17_CR26","unstructured":"Van Asch, V.: Macro-and micro-averaged evaluation measures. In: CLiPS, Belgium, vol. 49 (2013)"},{"key":"17_CR27","doi-asserted-by":"publisher","unstructured":"Benesty, J., Chen, J., Huang, Y., Doclo, S.: Study of the wiener filter for noise reduction. In: Speech Enhancement. Signals and Communication Technology. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/3-540-27489-8_2","DOI":"10.1007\/3-540-27489-8_2"}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86960-1_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T04:54:59Z","timestamp":1725771299000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86960-1_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030869595","9783030869601"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86960-1_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"11 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cagliari","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccsa.org\/","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":"Customed version of CyberChair 4","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1588","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":"466","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":"18","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":"29% - 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":"2,5","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":"8","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}