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DC Field Value Language Sohn, Myoung-Kyu - Kim, Hyunduk - 2022-10-21T08:00:01Z - 2022-10-21T08:00:01Z - 2022-10-21 - 2022-09 -
dc.identifier.issn 2586-0852 -
dc.identifier.uri -
dc.description.abstract As face recognition technology has become more common, image-based face authentification systems are being widely used. At the same time, techniques for hacking these technologies have begun to appear. Face spoofing, which attempts to attack the face recognition system by showing a printed face image, an on-screen image, or a 3D printed mask, etc. instead of the real thing, is a major problem in the face recognition system. In addition, as the technology of image recognition is advanced, the anti-spoofing method using it is gradually improving. 3D cameras or various expensive sensors may be used, but this has a problem of poor actual commercialization and scalability. To solve this problem, this paper intends to develop an anti-spoofing system using a single RGB sensor using deep learning-based recognition technology. It also proposes a system that operates in real-time by using a lighter model than the existing complex algorithm -
dc.language English -
dc.publisher Journal of Industrial Information Technology and Application -
dc.title Real-time Face Presentation Attack Detection with a Single RGB camera -
dc.type Article -
dc.identifier.doi 10.22664/ISITA.2021.6.3.610 -
dc.identifier.bibliographicCitation Journal of Industrial Information Technology and Application, v.6, no.3, pp.610 - 616 -
dc.description.isOpenAccess TRUE -
dc.subject.keywordAuthor deep learning -
dc.subject.keywordAuthor real-time face recognition -
dc.subject.keywordAuthor face anti-spoofing -
dc.subject.keywordAuthor presentation attack -
dc.subject.keywordAuthor face liveness detection -
dc.identifier.url -
dc.citation.endPage 616 -
dc.citation.number 3 -
dc.citation.startPage 610 -
dc.citation.title Journal of Industrial Information Technology and Application -
dc.citation.volume 6 -
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Appears in Collections:
Division of Automotive Technology 1. Journal Articles


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