Identity statement area
Reference TypeConference Proceedings
Last Update2018:
Metadata Last Update2020: administrator
Citation KeyBresanRezeBeluCarv:2018:PrAtDe
TitlePresentation Attack Detection by a Combination of Intrinsic Image Properties and a CNN
DateOct. 29 - Nov. 1, 2018
Access Date2020, Dec. 02
Number of Files1
Size1019 KiB
Context area
Author1 Bresan, Rodrigo
2 Rezende, Edmar
3 Beluzo, Carlos
4 Carvalho, Tiago
Affiliation1 Federal Institute of Sao Paulo (IFSP)
2 University of Campinas (UNICAMP)
3 Federal Institute of Sao Paulo (IFSP)
4 Federal Institute of Sao Paulo (IFSP), University of Campinas (UNICAMP)
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
Conference LocationFoz do Iguaçu, PR, Brazil
Book TitleProceedings
PublisherSociedade Brasileira de Computação
Publisher CityPorto Alegre
History2018-10-21 17:37:17 :: -> administrator ::
2020-02-20 22:06:51 :: administrator -> :: 2018
Content and structure area
Is the master or a copy?is the master
Document Stagecompleted
Document Stagenot transferred
Tertiary TypeUndergraduate Work
Keywordsbiometrics, presentation attack detection, PAD, intrinsic properties, anti-spoofing, facial recognition.
AbstractThe usage of face recognition for biometric systems has become widely adopted, since it allows the usage of a trait that is accessible to most of the people. Despite important progress on the field of face recognition, there is still a lack of works whose focus consists on the detection of presentation attacks. Presentation attacks occur when an imposter presents a synthetic sample in order to impersonate a valid user. For face biometric systems, this kind of attack is performed using a photograph, by playing a video of the user (commonly known as replay attack) or by making usage of 3D masks. Hereby, we propose a low-cost solution to detect these kind of attacks without the need of extra hardware. Our hypothesis is based on the fact that, through the extraction of intrinsic image properties, such as depth, saliency and illumination, it is possible to distinguish between a real biometric sample and a synthetic one. Performed experiments show that the proposed method achieved HTER values of 41.64% and 3.88% in inter and intra protocols respectively, achieving near state-of-the-art results.
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Next Higher Units8JMKD3MGPAW/3RPADUS
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