Face spoofing detection from single images using micro-texture analysis

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 differ-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. 1