Can Face Anti-spoofing Countermeasures Work In A Real World Scenario?

dc.creatorDe Freitas Pereira T.
dc.creatorAnjos A.
dc.creatorDe Martino J.M.
dc.creatorMarcel S.
dc.date2013
dc.date2015-06-25T19:13:36Z
dc.date2015-11-26T15:11:10Z
dc.date2015-06-25T19:13:36Z
dc.date2015-11-26T15:11:10Z
dc.date.accessioned2018-03-28T22:21:16Z
dc.date.available2018-03-28T22:21:16Z
dc.identifier9781479903108
dc.identifierProceedings - 2013 International Conference On Biometrics, Icb 2013. Ieee Computer Society, v. , n. , p. - , 2013.
dc.identifier
dc.identifier10.1109/ICB.2013.6612981
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84887421388&partnerID=40&md5=2dd85acfa629e30f228e23128156e51e
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/88901
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/88901
dc.identifier2-s2.0-84887421388
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1258069
dc.descriptionUser authentication is an important step to protect information and in this field face biometrics is advantageous. Face biometrics is natural, easy to use and less human-invasive. Unfortunately, recent work has revealed that face biometrics is vulnerable to spoofing attacks using low-tech equipments. This article assesses how well existing face anti-spoofing countermeasures can work in a more realistic condition. Experiments carried out with two freely available video databases (Replay Attack Database and CASIA Face Anti-Spoofing Database) show low generalization and possible database bias in the evaluated countermeasures. To generalize and deal with the diversity of attacks in a real world scenario we introduce two strategies that show promising results. © 2013 IEEE.
dc.description
dc.description
dc.description
dc.description
dc.descriptionCognitec,European FP7 Project Tabula Rasa,Safran-Morpho,Telefonica (Through Catedra UAM-Telefonica),Thales
dc.descriptionFlynn, P., Jain, A., Ross, A., (2008) Handbook of Biometrics, , Springer
dc.descriptionLi, S., Jain, A., (2011) Handbook of Face Recognition, , Springer
dc.descriptionDuc, M.B.Q., Your face is not your password (2009) Black Hat Conference
dc.descriptionUludag, U., Jain, A., Attacks on biometric systems: A case study in fingerprints (2004) Proc. SPIE-EI, pp. 622-633
dc.descriptionLeyden, J., Gummi bears defeat fingerprint sensors (2002) The Register, 16
dc.descriptionJohnson, P., Tan, B., Schuckers, S., Multimodal fusion vulnerability to non-zero effort (spoof) imposters (2010) Information Forensics and Security (WIFS), 2010 IEEE International Workshop on, pp. 1-5. , IEEE
dc.descriptionKanematsu, M., Takano, H., Nakamura, K., Highly reliable liveness detection method for iris recognition (2007) SICE, 2007 Annual Conference, pp. 361-364. , IEEE
dc.descriptionTan, X., Li, Y., Liu, J., Jiang, L., Face liveness detection from a single image with sparse low rank bilinear discriminative model (2010) Computer Vision-ECCV 2010, pp. 504-517
dc.descriptionZhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., Li, S., A face antispoofing database with diverse attacks (2012) Biometrics (ICB), 2012 5th IAPR International Conference on Biometrics, pp. 26-31. , IEEE
dc.descriptionChingovska, I., Anjos, A., Marcel, S., On the effectiveness of local binary patterns in face anti-spoofing (2012) IEEE BIOSIG 2012, , Sept
dc.descriptionChakka, M., Anjos, A., Marcel, S., Tronci, R., Competition on counter measures to 2-d facial spoofing attacks (2011) Proceedings of IAPR IEEE International Joint Conference on Biometrics (IJCB), , Washington DC, USA, Oct
dc.descriptionPan, G., Sun, L., Wu, Z., Lao, S., Eyeblink-based antispoofing in face recognition from a generic webcamera (2007) Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on Computer Vision, pp. 1-8. , IEEE
dc.descriptionKollreider, K., Fronthaler, H., Bigun, J., Non-intrusive liveness detection by face images (2009) Image and Vision Computing, 27 (3), pp. 233-244
dc.descriptionAnjos, A., Marcel, S., Counter-measures to photo attacks in face recognition: A public database and a baseline (2011) International Joint Conference on Biometrics 2011, , Oct
dc.descriptionLi, J., Wang, Y., Tan, T., Jain, A., Live face detection based on the analysis of fourier spectra (2004) Biometric Technology for Human Identification, 5404, pp. 296-303
dc.descriptionMaatta, J., Hadid, A., Pietikaandinen, M., Face spoofing detection from single images using texture and local shape analysis (2012) Biometrics, IET, 1, pp. 3-10. , march
dc.descriptionTrefny, J., Matas, J., Extended set of local binary patterns for rapid object detection (2010) Proceedings of the Computer Vision Winter Workshop, 2010
dc.descriptionDe Freitas Pereira, T., Anjos, A., De Martino, J.M., Marcel, S., LBP-TOP based countermeasure against facial spoofing attacks (2012) International Workshop on Computer Vision with Local Binary Pattern Variants - ACCV, , Nov
dc.descriptionAnjos, A., Shafey, L.E., Wallace, R., Günther, M., Mc-Cool, C., Marcel, S., Bob: A free signal processing and machine learning toolbox for researchers (2012) 20th ACM Conference on Multimedia Systems (ACMMM), , Nara, Japan, ACM Press, Oct
dc.descriptionFroba, B., Ernst, A., Face detection with the modified census transform (2004) Automatic Face and Gesture Recognition, 2004. Proceedings. Sixth IEEE International Conference on, pp. 91-96. , IEEE
dc.descriptionKomulainen, H.A.P.M.J., Face spoofing detection using dynamic texture (2012) International Workshop on Computer Vision with Local Binary Pattern Variants - ACCV, , Nov
dc.descriptionKuncheva, L.I., Whitaker, C.J., Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy (2003) Mach. Learn., 51, pp. 181-207. , May
dc.languageen
dc.publisherIEEE Computer Society
dc.relationProceedings - 2013 International Conference on Biometrics, ICB 2013
dc.rightsfechado
dc.sourceScopus
dc.titleCan Face Anti-spoofing Countermeasures Work In A Real World Scenario?
dc.typeActas de congresos


Este ítem pertenece a la siguiente institución