Face Anti-Spoofing Detection Using Structure-Texture Decomposition

Abstract

A key area in computer vision and biometric authentication systems is detecting and classifying face anti-spoofing. The novel method for face anti-spoofing presented in this paper focuses on color invariant methods. The proposed approach consists to compare two different feature extraction methods: LBP and HOG, used with two different machine-learning models such as KNN and SVM. The suggested method improves the system's ability to discriminate by utilizing color properties and overcoming obstacles like varying lighting and image quality. The structure-texture decomposition is used as a feature that is used to improve the performance of the face anti-spoofing methods. After the experimental results using different techniques, we noticed that structure-texture decomposition can be a good feature for face anti-spoofing detection features.