Face spoofing detection from single images using micro-texture analysis

TLDR

Face biometric systems are vulnerable to spoofing attacks, which involve masquerading by falsifying data, and face prints exhibit detectable printing defects that can be identified via texture features. The study proposes a novel texture‑analysis approach to detect spoofing by distinguishing live faces from printed ones. The method analyzes facial image textures with multi‑scale local binary patterns to differentiate live faces from prints. Experiments on a public database show that the LBP‑based texture method is robust, fast, user‑free, and achieves superior spoofing detection performance while also enabling simultaneous face recognition.

Abstract

Current face biometric systems are vulnerable to spoo 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 assessment, characterization of printing artifacts, and differences in light reflection, we propose to approach the problem 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 analyzes 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 texture features that are used for spoofing detection can also be used for face recognition. This provides a unique feature space for coupling spoofing detection and face recognition. Extensive experimental analysis on a publicly avail able database showed excellent results compared to existing works.