
Detection of Face Spoofing using Color Texture and Edge Features
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.e1008.0285s20
Subject(s) - spoofing attack , computer science , artificial intelligence , histogram , face (sociological concept) , computer vision , facial recognition system , local binary patterns , focus (optics) , enhanced data rates for gsm evolution , pattern recognition (psychology) , computer security , image (mathematics) , social science , physics , sociology , optics
The wide scale use of facial recognition systems has caused concerns about spoofing attacks. Security is essential requirement for a face recognition system to provide reliable protection against spoofing attacks. Spoofing happens in situations where someone tries to behave as an authorized user to obtain illicitly access the protected system to gain advantage over it. In order to identify spoofing attacks, face spoofing detection approaches have been used. Traditional face spoofing detection techniques are not good enough as most of them focus only on the gray scale information and discarding the color information. Here a face spoofing detection approach with color texture and edge analysis is presented. The approach for investigating the texture of input images, Local binary pattern and Edge Histogram descriptor are proposed. Experiments on a publicly available dataset, Replay attack, showed excellent results compared to existing works.