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 [BibTeX] [Marc21]
Face liveness detection using dynamic texture
Type of publication: Journal paper
Citation: deFreitasPereira_EURASIPJIVP_2014
Publication status: Published
Journal: EURASIP Journal on Image and Video Processing
Volume: 2
Year: 2014
Month: January
URL: https://pypi.python.org/pypi/a...
DOI: 10.1186/1687-5281-2014-2
Abstract: User authentication is an important step to protect information, and in this context, face biometrics is potentially advantageous. Face biometrics is natural, intuitive, easy to use, and less human-invasive. Unfortunately, recent work has revealed that face biometrics is vulnerable to spoofing attacks using cheap low-tech equipment. This paper introduces a novel and appealing approach to detect face spoofing using the spatiotemporal (dynamic texture) extensions of the highly popular local binary pattern operator. The key idea of the approach is to learn and detect the structure and the dynamics of the facial micro-textures that characterise real faces but not fake ones. We evaluated the approach with two publicly available databases (Replay-Attack Database and CASIA Face Anti-Spoofing Database). The results show that our approach performs better than state-of-the-art techniques following the provided evaluation protocols of each database.
Keywords: Anti-spoofing, Counter-Measures, Face Recognition, temporal pattern extraction, Texture Analysis
Authors de Freitas Pereira, Tiago
Komulainen, Jukka
Anjos, André
De Martino, José Mario
Hadid, Abdenour
Pietikainen, Matti
Marcel, Sébastien
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  • deFreitasPereira_EURASIPJIVP_2014.pdf