{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:16:30Z","timestamp":1760217390959,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2015,6,19]],"date-time":"2015-06-19T00:00:00Z","timestamp":1434672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT - Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia Portugal","award":["SFRH\/BD\/74263\/2010"],"award-info":[{"award-number":["SFRH\/BD\/74263\/2010"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fingerprint liveness detection methods have been developed as an attempt to overcome the vulnerability of fingerprint biometric systems to spoofing attacks. Traditional approaches have been quite optimistic about the behavior of the intruder assuming the use of a previously known material. This assumption has led to the use of supervised techniques to estimate the performance of the methods, using both live and spoof samples to train the predictive models and evaluate each type of fake samples individually. Additionally, the background was often included in the sample representation, completely distorting the decision process. Therefore, we propose that an automatic segmentation step should be performed to isolate the fingerprint from the background and truly decide on the liveness of the fingerprint and not on the characteristics of the background. Also, we argue that one cannot aim to model the fake samples completely since the material used by the intruder is unknown beforehand. We approach the design by modeling the distribution of the live samples and predicting as fake the samples very unlikely according to that model. Our experiments compare the performance of the supervised approaches with the semi-supervised ones that rely solely on the live samples. The results obtained differ from the ones obtained by the more standard approaches which reinforces our conviction that the results in the literature are misleadingly estimating the true vulnerability of the biometric system.<\/jats:p>","DOI":"10.3390\/s150614615","type":"journal-article","created":{"date-parts":[[2015,6,19]],"date-time":"2015-06-19T10:23:33Z","timestamp":1434709413000},"page":"14615-14638","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Fingerprint Liveness Detection in the Presence of Capable Intruders"],"prefix":"10.3390","volume":"15","author":[{"given":"Ana","family":"Sequeira","sequence":"first","affiliation":[{"name":"INESC TEC\u2014INESC Technology and Science, Campus da FEUP, Rua Dr. Roberto Frias, Porto 4200-465, Portugal"},{"name":"Departamento de Engenharia Eletrot\u00e9cnica e de Computadores, Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, Porto 4200-465, Portugal"}]},{"given":"Jaime","family":"Cardoso","sequence":"additional","affiliation":[{"name":"INESC TEC\u2014INESC Technology and Science, Campus da FEUP, Rua Dr. Roberto Frias, Porto 4200-465, Portugal"},{"name":"Departamento de Engenharia Eletrot\u00e9cnica e de Computadores, Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, Porto 4200-465, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2015,6,19]]},"reference":[{"key":"ref_1","first-page":"52","article-title":"Quality Measures in Biometric Systems","volume":"10","author":"Fierrez","year":"2012","journal-title":"IEEE Secur. Priv."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Maltoni, D., Maio, D., Jain, A.K., and Prabhakar, S. (2009). Handbook of Fingerprint Recognition, Springer.","DOI":"10.1007\/978-1-84882-254-2"},{"key":"ref_3","unstructured":"Sandstr\u00f6m, M. (2004). Liveness Detection in Fingerprint Recognition Systems. [Master Thesis, Link\u00f6 pings Universitet]."},{"key":"ref_4","unstructured":"Yun, Y.W. (2002). The \u201c123\u201d of biometric technology. Synth. J., 83\u201396. Available online: http:\/\/www.cp.su.ac.th\/rawitat\/teaching\/forensicit06\/coursefiles\/files\/biometric.pdf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1049\/iet-bmt.2013.0020","article-title":"Presentation attack detection methods for fingerprint recognition systems: A survey","volume":"3","author":"Sousedik","year":"2014","journal-title":"IET Biom."},{"key":"ref_6","unstructured":"Van der Putte, T., and Keuning, J. (2000). Smart Card Research and Advanced Applications, Springer."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1007\/978-3-540-74549-5_76","article-title":"Vitality Detection from Fingerprint Images: A Critical Survey","volume":"Volume 4642","author":"Lee","year":"2007","journal-title":"Advances in Biometrics"},{"key":"ref_8","unstructured":"Yurish, S. Fingerprint Sensors: Liveness Detection and Hardware Solutions. Sensors and Biosensors, MEMS Technologies and its Applications, Volume 2, 121\u2013148."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Nikam, S.B., and Agarwal, S. (2008, January 16\u201318). Texture and wavelet-based spoof fingerprint detection for fingerprint biometric systems. Nagpur, India.","DOI":"10.1109\/ICETET.2008.134"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1007\/978-3-540-88458-3_100","article-title":"Gabor filter-based fingerprint anti-spoofing","volume":"Volume 5259","author":"Nikam","year":"2008","journal-title":"Advanced Concepts for Intelligent Vision Systems"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Nikam, S.B., and Agarwal, S. (2008, January 30\u201331). Wavelet energy signature and GLCM features-based fingerprint anti-spoofing. Hong Kong, China.","DOI":"10.1109\/ICWAPR.2008.4635872"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Nikam, S., and Agarwal, S. (2008, January 26\u201328). Fingerprint liveness detection using curvelet energy and co-occurrence signatures. Penang, Malaysia.","DOI":"10.1109\/CGIV.2008.9"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Frassetto Nogueira, R., de Alencar Lotufo, R., and Campos Machado, R. (2014, January 17). Evaluating software-based fingerprint liveness detection using Convolutional Networks and Local Binary Patterns. Rome, Italy.","DOI":"10.1109\/BIOMS.2014.6951531"},{"key":"ref_14","unstructured":"Ghiani, L., Marcialis, G.L., and Roli, F. (2012, January 11\u201315). Fingerprint liveness detection by local phase quantization. Tsukuba, Japan."},{"key":"ref_15","unstructured":"Yambay, D., Ghiani, L., Denti, P., Marcialis, G.L., Roli, F., and Schuckers, S. (April, January 29). LivDet 2011 Fingerprint liveness detection competition. New Delhi, India."},{"key":"ref_16","unstructured":"Ghiani, L., Hadid, A., Marcialis, G.L., and Roli, F. (October, January 29). Fingerprint Liveness Detection using Binarized Statistical Image Features. Arlington, VA, USA."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gragnaniello, D., Poggi, G., Sansone, C., and Verdoliva, L. (2013, January 9). Fingerprint liveness detection based on Weber Local image Descriptor. Napoli, Italy.","DOI":"10.1109\/BIOMS.2013.6656148"},{"key":"ref_18","unstructured":"Marcialis, G.L., Lewicke, A., Tan, B., Coli, P., Grimberg, D., Congiu, A., Tidu, A., Roli, F., and Schuckers, S. (2009). Image Analysis and Processing\u2014ICIAP 2009, Springer."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.future.2010.11.024","article-title":"A high performance fingerprint liveness detection method based on quality related features","volume":"28","author":"Galbally","year":"2012","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s11235-010-9316-0","article-title":"Evaluation of direct attacks to fingerprint verification systems","volume":"47","author":"Galbally","year":"2011","journal-title":"Telecommun. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2845","DOI":"10.1016\/j.patcog.2010.01.023","article-title":"Spoofing protection for fingerprint scanner by fusing ridge signal and valley noise","volume":"43","author":"Tan","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_22","unstructured":"Tan, B., and Schuckers, S. (2006, January 17\u201322). Liveness detection for fingerprint scanners based on the statistics of wavelet signal processing. New York, NY, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1268","DOI":"10.1049\/el.2010.1549","article-title":"Automatic detection of active sweat pores of fingerprint using highpass and correlation filtering","volume":"46","author":"Manivannan","year":"2010","journal-title":"Electron. Lett."},{"key":"ref_24","unstructured":"Johnson, P., and Schuckers, S. (2014, January 10\u201312). Fingerprint pore characteristics for liveness detection. Darmstadt, Germany."},{"key":"ref_25","unstructured":"Gottschlich, C., Marasco, E., Yang, A.Y., and Cukic, B. (October, January 29). Fingerprint liveness detection based on histograms of invariant gradients. Clearwater, FL, USA."},{"key":"ref_26","first-page":"1643","article-title":"Wavelet Based Fingerprint Liveness Detection","volume":"2","author":"Warwante","year":"2012","journal-title":"Int. J. Eng. Res. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1109\/TIFS.2015.2398817","article-title":"Deep Representations for Iris, Face, and Fingerprint Spoofing Detection","volume":"10","author":"Menotti","year":"2015","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Quionero-Candela, J., Sugiyama, M., Schwaighofer, A., and Lawrence, N.D. (2009). Dataset Shift in Machine Learning, The MIT Press.","DOI":"10.7551\/mitpress\/9780262170055.001.0001"},{"key":"ref_29","unstructured":"Marasco, E., and Sansone, C. (2011). On the Robustness of Fingerprint Liveness Detection Algorithms against New Materials used for Spoofing, BIOSIGNALS."},{"key":"ref_30","unstructured":"Rattani, A., and Ross, A. (October, January 29). Automatic adaptation of fingerprint liveness detector to new spoof materials. Clearwater, FL, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1145\/1541880.1541882","article-title":"Anomaly detection: A survey","volume":"41","author":"Chandola","year":"2009","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/MC.2014.118","article-title":"Cosmetic Contact Lenses and Iris Recognition Spoofing","volume":"47","author":"Bowyer","year":"2014","journal-title":"Computer"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ghiani, L., Yambay, D., Mura, V., Tocco, S., Marcialis, G.L., Roli, F., and Schuckers, S. (2013, January 4\u20137). LivDet 2013 Fingerprint liveness detection competition. Madrid, Spain.","DOI":"10.1109\/ICB.2013.6613027"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ferreira, P.M., Sequeira, A.F., and Rebelo, A. (2015, January 17\u201319). A Fuzzy C-Means Algorithm for Fingerprint Segmentation. Santiago de Compostela, Spain.","DOI":"10.1007\/978-3-319-19390-8_28"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Fahmy, M., and Thabet, M. (2013, January 12\u201315). A fingerprint segmentation technique based on morphological processing. Athens, Greece.","DOI":"10.1109\/ISSPIT.2013.6781882"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, H., Sun, Z., and Tan, T. (2010, January 23\u201326). Contact lens detection based on weighted LBP. Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.1040"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","article-title":"Multiresolution gray-scale and rotation invariant texture classification with local binary patterns","volume":"24","author":"Ojala","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/0031-3203(95)00067-4","article-title":"A comparative study of texture measures with classification based on featured distributions","volume":"29","author":"Ojala","year":"1996","journal-title":"Pattern Recognit."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Lowe, D.G. (1999, January 20\u201327). Object recognition from local scale-invariant features. Kerkyra, Greece.","DOI":"10.1109\/ICCV.1999.790410"},{"key":"ref_40","unstructured":"Vedaldi, A., and Fulkerson, B. (2010). Proceedings of the International Conference on Multimedia, ACM."},{"key":"ref_41","unstructured":"Wei, Z., Qiu, X., Sun, Z., and Tan, T. (2008, January 8\u201311). Counterfeit iris detection based on texture analysis. Tampa, FL, USA."},{"key":"ref_42","unstructured":"Haralick, R., Shanmugam, K., and Dinstein, I. Textural features for image classification. Available online: http:\/\/haralick.org\/journals\/TexturalFeatures.pdf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1162\/089976601750264965","article-title":"Estimating the support of a high-dimensional distribution","volume":"13","author":"Platt","year":"2001","journal-title":"Neural Comput."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1016\/S0167-8655(99)00087-2","article-title":"Support vector domain description","volume":"20","author":"Tax","year":"1999","journal-title":"Pattern Recognit. Lett."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"27:1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_47","unstructured":"Nabney, I. (2002). NETLAB: Algorithms for Pattern Recognition, Springer."},{"key":"ref_48","unstructured":"International Organization for Standardization (2014). Information Technology-Biometrics-Presentation Attack Detection-Part 3: Testing, Reporting and Classification of Attacks, International Organization for Standardization."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Bordallo L\u00f3pez, M., Nieto, A., Boutellier, J., Hannuksela, J., and Silv\u00e9n, O. (2014). Evaluation of real-time LBP computing in multiple architectures. J. Real-Time Image Process., 1\u201322.","DOI":"10.1007\/s11554-014-0410-5"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/6\/14615\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:48:08Z","timestamp":1760215688000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/6\/14615"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,6,19]]},"references-count":49,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2015,6]]}},"alternative-id":["s150614615"],"URL":"https:\/\/doi.org\/10.3390\/s150614615","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2015,6,19]]}}}