Current face biometric systems are vulnerable to spoof ing attacks. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby gaining illegitimate access. Inspired by image quality as sessment, characterization of printing artifacts, and dif f er ences in light reflection, we propose to approach the prob lem of spoofing detection from texture analysis point of view. Indeed, face prints usually contain printing quality defects that can be well detected using texture features. Hence, we present a novel approach based on analyzing facial image textures for detecting whether there is a live person in front of the camera or a face print. The proposed approach ana lyzes the texture of the facial images using multi-scale local binary patterns (LBP). Compared to many previous works, our proposed approach is robust, computationally fast and does not require user-cooperation. In addition, the tex ture features that are used for spoofing detection can also be used for face recognition. This provides a unique fea ture space for coupling spoofing detection and face recog nition. Extensive experimental analysis on a publicly avail able database showed excellent results compared to existing works.
Inspired by image quality assessment, the properties of printing artifacts and differences in light reflection that can be detected using texture features, the work in REF analyses the texture of facial images using multiscale local binary patterns operators.
Face spoofing detection from single images using micro-texture analysis
2011 International Joint Conference on Biometrics (IJCB)
2011 International Joint Conference on Biometrics (IJCB)
Economics Computer Science